Artificial Intelligence and Predictive Analytics

ARTICLE

Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data Varadraj P. Gurupura, Shrirang A. Kulkarnib, Xinliang Liua, Usha Desai c and Ayan Nasird

aDepartment of Health Management and Informatics, University of Central Florida, Orlando, FL, USA; bSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; cDepartment of Electronics and Communication Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, Udupi, India; dUCF School of Medicine, University of Central Florida, Orlando, FL, USA

ABSTRACT Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three- hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive ana- lysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multi- variate LR analysis.

ARTICLE HISTORY Received 16 April 2018 Accepted 30 August 2018

KEYWORDS Deep Learning Technique (DLT); medicare data; Machine Learning (ML); Linear Regression (LR); Confusion Matrix (CM)

Introduction

Methods involving Artificial Intelligence (AI) associated with Deep Learning Technique (DLT) and Machine Learning (ML) are slowly but surely being used in medical and health infor- matics. Traditionally, techniques such as Linear Regression (LR) (Nimon & Oeswald, 2013), Analysis of Variance (ANOVA) (Kim, 2014), and Multivariate Analysis of Variance (MANOVA) (Xu, 2014) (Malehi et al., 2015) have been used for predicting outcomes in healthcare. However, in the recent years the methods of analysis applied are changing towards the aforementioned computationally stronger techniques. The purpose of current research work delineated in this paper, effectively proves the usefulness of DLTs and Confusion Matrix (CM)

CONTACT Usha Desai [email protected] Electronics and Communication Engineering, Nitte Mahalinga Adyanthaya Memorial Institute of Technology, Nitte, India

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2019, VOL. 31, NO. 1, 99–115 https://doi.org/10.1080/0952813X.2018.1518999

© 2018 Informa UK Limited, trading as Taylor & Francis Group

analysis to predict the outcome for a healthcare informatics case study. The core objectives of this research are as follows:

a) Illustrate the power of DLT (LeCun, et al., 2015) by conducting an analysis comparing it with Linear Regression (LR).

b) Introduce advancement in science of DLT by mathematical formulations. c) To analyse that, if changes applied in DLT algorithm can affect the predictability involved.

To achieve the aforementioned objectives, investigators conducted experimentation on a dataset that was constructed based on the 2014 Medicare Provider Utilization and Payment Data. This data encompasses information on services provided to Medicare beneficiaries by physical therapists. The 2014 Medicare Provider Utilization and Payment Data provide informa- tion on procedures and services provided to those insured under Medicare by various healthcare professionals. This dataset has information on utilisation, amount differentiated into allowed amount and the Medicare payment (Medicare Provider and Utilization Data, Online 2018), and charges submitted which are organised and identified by a Medicare assigned National Provider Identifier. It is important to mention that this data covers only those claims covered for the Medicare fee-for-service population (specifically 100% final- action physician/supplier Part B non-institutional line items).

In the past, research experiments on Medicare data have been successfully carried out by using methods such as LR; however, while proposed study applies DLT to satisfy the aforementioned core research objectives. Additionally, we have compared the obtained results of DLT and LR. Thereby, ascertaining the strength and usefulness of this stronger computational technique in analysing the Medicare data.

Related work

In recent years, Machine Learning (ML)/Artificial Intelligence (AI) approaches are widely adopted by the researchers to solve a variety of complex problems. Traditional ML/AI approaches have been widely adopted in applications like image processing, signal evaluation, pattern recognition, etc. For large datasets, the traditional ML/AI approaches sometimes may provide the erroneous results. Hence, in recent years, the large volumes of data have been efficiently processed and interpreted using a modernised ML using DLT.

The DLT can be implemented by means of the Neural Network (NN) approach or Belief Network (BN) approach. In the literature, the NN-based DLT, such as Deep NN (DNN) and Recurrent NN (RNN) are widely implemented to process the medical dataset, in order to get better accuracy. The results of previous study also confirm that, DLT approaches will offer better result in disease recognition, classification and evaluation approaches. Due to its superiority, it is widely adopted by the researchers to evaluate the dataset related to patient’s health information. In the proposed work, evaluation of the aforementioned dataset is carried using the DLT to develop a health information system, which is applicable to analyse the public health data.

Suinesiaputra (Suinesiaputra, Gracia, Cowan, & Young, 2015) proposed a detailed review regarding the heart disease by using the benchmark cardiovascular image dataset. This work also insists the necessity of sharing the medical data in order to predict the cardiovascular disease (CVD) in its early stages (Zhang et al., 2016). In addition to this, the work of Puppala (Puppala et al., 2015) proposes a novel online evaluation framework for the CVD dataset using an approach termed as Methodist Environment for Translational Enhancement and Outcomes Research (METEOR). This framework is considered to construct a data warehouse (METEOR) to link the patient’s dataset with the end users, such as the doctors and research- ers. In order to test the efficiency of the proposed approach, breast cancer dataset was

100 V. P. GURUPUR ET AL.

chosen for evaluation purposes. The result of this approach confirms the efficiency of METEOR in data collection, sharing, disease detection and treatment planning procedures.

It is important to note that Santana (Santana et al., 2012) proposed an evaluation tool to evaluate the heart risk based on the patient’s health information. The developed tool (Santana et al., 2012) collects invasive/non-invasive health information from the patient, and provides the disease related information to support the treatment planning process. The research contribution by Snee and McCormick (2004) proposes an approach to consider the indispensable elements of the available public health information network to collect and forecast the data for Disease Control and Prevention centres. This work clearly presents the software and hardware requirements, to accomplish the proposed setup to link the patient with the monitoring system. Web based online examination procedure was proposed by (Weitzel, Smith, Deugd, & Yates, 2010). In this framework, the concept of cloud computing is implemented to enhance the communal collaborative pattern to support a physician to employ protocols while accessing, assembling and visualising patient data through embeddable web applications coined as OpenSocial gadgets. This DLT framework supports real time interaction between the patient and the doctor for purposes of diagnosis and treatment.

The investigators would like to mention that Zhang (Zhang, Zheng, Lin, Zhang, & Zhou, 2013) proposed a prediction model for the CVD based on various signals collected using the dedicated sensors. This work considers the use of wearable sensors to collect the signals from the chosen parts of the human body and non-invasive imaging techniques to identify the disease initiations required to develop models to support the early detection of CVD. The recent research work by Zheng (Zheng et al., 2014) also confirms the need for the use of these wearable sensors to support the premature detection of the disease. This work exem- plifies the use of wireless/wire based biomedical sensors in association with DLT to collect critical data from internal/external organs of the human body in order to make an accurate prediction on the disease.

DLT is also applied to support the early detection of life threatening diseases that aids the reduction of mortality rates. The availability of modern clinical equipment and the data sharing network reduced the gap between the patients and the doctor in identifying the disease, getting the opinion from the experts, comparing the existing patient’s critical data pertaining to the disease with the related data existing in the literature, identifying the severity/stage of the disease, and possible treatment procedures. Hence, in recent years, more researchers are working in the field of health informatics using DLT to propose efficient data sharing frameworks, modifying the existing health informatics setups, and synthesising wearable health devices to track the normal/ abnormal body signals to predict the disease.

Usually in health informatics, the size of the dataset could be large and the accuracy of disease identification and the evaluation procedure relies mainly on the processing approach considered to evaluate the healthcare data. Here the accuracy of the disease prediction depends only on the processing approach. The recent work of (Ravi et al., 2017) summarises the implementation of the applications of various deep learning approaches to evaluate a healthcare database.

Methodology

Figure 1 represents the flow diagram of Medicare dataset pre-processing system using Python simulation tool. Further, pre-processed data is subjected for classification using DLT and LR algorithms. Our research method relies on the use of LR to test two particular outcome variables. We then proceed with the application of DLT and perform a required comparison to satisfy the aforementioned research objectives. This encourages us to test a simple prediction model using linear regression to indicate towards the property of homoscedasticity. Further in the required analysis the investigators consider a simple linear regression model as given in Equation (1).

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 101

Y ¼ pþ q Z (1)

where Y is the outcome, and variable Z is the predictor variable,q identifies the slope and p is the intercept. The simulation of the proposed block diagram (Figure 2) was implemented in Python 3.6 using packages such as pandas, scipy and sklearn modules. The metric considered was R2 .

R2 ¼ 1� SSre SSto

(2)

R2 indicates the correlation coefficient squared where SSre known as error sum of squares and SSto known as total corrected sum of squares as given using Equations (3) and (4), respectively.

SSre ¼ Xn i¼1

yi � ŷið Þ2 (3)

SSto ¼ Xn i¼1

yi � �yið Þ2 (4)

In the Equations (3) and (4) �yi estimates the mean value, whereas ŷi gives the mean value of yi in the regression structure, respectively. Whereas, the multiple LR was modelled using Equation (5),

y ¼ X1n1 þ X2n2 þ X3n3 þ���� þ Xpnpyþ 2 (5)

where y is the dependent variable and X1; 2; X3 and so on, are the p independent variables with parameters n1; n2 ,n3 and so on. In applying DLT, we first base our premise on mathematical formulation, formulated by implementation and discussion of results. Figure 2 represents stages involved in development of proposed DLT Medicare utilisation informatics system.

Mathematical formulation for DLT algorithm

In this study, the investigators would first like to illustrate the DLT algorithms used for the proposed Medicare health data informatics system. To specify this in algorithmic form, the Stochastic Gradient Descent (SGD) algorithm is considered as described in Figure 3. The key part

Importing Libraries Importing the

Dataset

Categorical Data is

Encoded

Splitting the Dataset

into Train and Test

Set

Perform Feature

Scaling on Train and

Test Set

PRE-PROCESSING DATA

Figure 1. Flow diagram for pre-processing of the medicare utilisation dataset.

102 V. P. GURUPUR ET AL.

in this algorithm is the calculation of the partial derivatives @Lk=@w . If ∂Lk= @wið Þ is positive, further increasing wi by some small amount will increase the loss Lk for the current example; decreasing wi will decrease the loss function (Taylor, 1993), (Fernandes, Gurupur, et al., 2017). In this study, a small step is considered in the direction to minimise the loss function, as an efficient deep learning function.

Input: Network parameters , loss function , training data , learning rate >

while termination conditions are not met, perform as follow:

( , ) ← .

( ) ← ( , )

← ( , , , )

end

Figure 3. Implementation flow for the Stochastic Gradient Descent (SGD) algorithm.

Randomly initialize

the weights to

numbers

Input the first

patient record

details from the

database to the

input layer

Each feature of the

database is

associated to one

input node

Forward

propagation is

performed from left

to right

Error obtained is

calculated

Predicted result is

compared with

actual result

Activation is

propagated until the

predicted result ‘y’ is

obtained

Neurons are

activated such that

the impact of each

neuron’s activation

is limited by weights

The previous steps

updated the weight

for each observation

in the dataset

Weights are updated

according to the

calculated weight

Error id back

propagated

Perform back

propagation from

right to left

The entire process is

repeated for the

entire training

(epoch).

Redo the process for

more epochs

Figure 2. Methodology in implementation of proposed medicare data analyser system.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 103

Backpropagation in a multilayer perceptron

In this work, a simple multilayer perceptron with a standard fully connected feed-forward neural network layer along with the sum of squared error loss function (Zheng et al., 2014) (Figure 4) is considered as follows (Zhang et al., 2016):

L y; ŷð Þ ¼ XN i¼1

ðyi � ŷiÞ2 (6)

where N is the number of outputs, yi is the ith label, and ŷi = output (w, f) is the network’s prediction of yi , given the feature vector f and current parameter w.

Here the input vector to the current layer is the vector zi (of length 4), the element-wise nonlinearity (activation function, such as tanh and sigmoid), then the forward-pass equations for this network are (Zhang et al., 2016) expressed as follows:

zi¼bi þ X4 j¼1

wi;jai (7)

ŷi ¼ σ zið Þ (8)

where bi is the bias and wi;j is the weight connecting input i to neuron j as shown in Figure 5. Given the loss function, the first partial derivative is calculated with respect to the network output,byi (Taylor, 1993):

ð@LkÞ=ð@ŷjÞ ¼ @=ð@ŷjÞð XN

ði¼1Þ ðyi � ŷiÞ2Þ (9)

a1

a2

a13

b1

b2

b13

y

INPUT LAYER

HIDDEN LAYER 1 HIDDEN LAYER 2

OUTPUT LAYER

X1

X2

X3

X30

Figure 4. Application of Stochastic Gradient Descent deep learning computation.

104 V. P. GURUPUR ET AL.

¼ @

@ŷj ðyj � ŷjÞ2 (10)

¼ �2ðyj � ŷjÞ (11)

Following the network structure backward, the @Lk @zi

is a function of @Lk @ŷi

is computed (Ravi et al., 2017). This will depend on the mathematical form of the activation function σk zð Þ (Taylor, 1993) in which sigmoid activation function is considered.

@Lk @zi

¼ @Lk @ŷi

@ŷi @zi

(12)

¼ σ0k zið Þ @Lk @ŷi

(13)

where σk zð Þ ¼ 1 1þe�z and the function σ

0 k zð Þ ¼ σk zð Þ 1� σk zð Þð Þ.

Next, applying the chain rule to calculate the partial derivatives of the weights wj;i given the

previously calculated derivatives, @Lk @zi

(Fernandes, Gurupur, et al., 2017),

@Lk @wj;i

¼ X3 k¼1

@Lk @zi

@zi @wj;i

(14)

¼ @Lk @zi

@zi @wj;i

(15)

X1

X2

X3

X30

Z

Y

W3

Actual Value

Output Value

½ (z-y) 2

Figure 5. Assigning the weights to the artificial neural network.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 105

¼ @Lk @zi

@zi @wj;i

bi þ X4 k¼1

wk;iai

! (16)

¼ ai @Lk @zi

(17)

Finally, derivatives of the loss function is computed with respect to the input activation ai , where @Lk @zi

given as,

@Lk @ai

¼ X3 j¼1

@Lk @zj

@zj @ai

(18)

¼ X3 j¼1

@Lk @zj

@

@ai ðbj þ

X4 k¼1

wk;jajÞ (19)

¼ X3 j¼1

@Lk @zj

wi;j (20)

Outcome variables

To apply Machine Learning (Martis, Lin, Gurupur, & Fernandes, 2017) (Fernandes, Chakraborty, Gurupur, & Prabhu, 2016) (Fernandes, Gurupur, Sunder, & Kadry, 2017) (Rajnikanth, Satapathy, et al, 2017) and Deep Learning (Shabbira, Sharifa, Nisara, Yasmina, & Fernandes, 2017) (Khan, Sharif, Yasmin, & Fernandes, 2016) (Hempelmann, Sakoglu, Gurupur, & Jampana, 2015) (Walpole, Myers, Myers, & Ye, 2012) (Kulkarni & Rao, 2009), we obtained the aforementioned dataset with information on 40,000 physical therapists from the aforementioned 2014 Medicare Provider Utilization and Payment Data. In the dataset we added a new column termed as Result which contains the value resulted by comparison of the Total Medicare standardized Payment Value with its median value. Result column consists of two values (0, 1) for the following outcome variables:

Outcome-1 (O1): Result = 1 {when Medicare Standardized Payment Received by a Physical Therapist is greater than the median} Result = 0 {when Medicare Standardized Payment Received by a Physical Therapist is equal to or less than the median}

Outcome-2 (O2): Result = 1 {when Total Medicare Standardized Payment Value is greater than Median Household Value} Result = 0 {when Total Medicare Standardized Payment Value is lesser than Median Household Value}

Here we would like to note that for Outcome-2 the investigators have used multiple dependent variables and a single independent variable. For the purposes of experimentation with DLT we have applied Spyder V3 on Ubuntu operating system. The respective algorithm implemented in the proposed experimentation is illustrated in Figure 6.

106 V. P. GURUPUR ET AL.

Results and discussion

Results

The investigators first analysed both the aforementioned outcome variables using linear regression. Thus to visualise the data we further plotted a scatter plot of resulting data values. In this study, the simulation plot of distribution of results is depicted in Figure 7. In which, the scatter plot distribution Figure 7(a) shows signs of non-linearity and thus the principle of homoscedasticity was disapproved. This is because homoscedasticity would have required evenly distributed values; thereby leading the investigators to further this investiga- tion using a range of independent variables to predict the Total Medicare Standardized Payment Value (dependent variable) (Diehr et al., 1999). For this purpose the investigators applied multiple LR model with the dependent variable as Total Medicare Standardized Payment Value. The range of independent variables was derived by stepwise regression. The default p value considered for eliminating independent variables entering the set was 15% (0.15). The comparative plot of predicted values and the actual values is illustrated in Figure 7 (b). Our results achieved R2 as 0.9451 which in a way indicated that the explained variance was around 94%. To further visualise it, we plotted a scatterplot as illustrated in Figure 7(b) for multiple LR analysis.

The scatter plot depicted in Figure 7(b) using multiple LR indicates heteroscedasticity of data values. Heteroscedasticity has a major impact on regression analysis. The presence of heterosce- dasticity can invalidate the significance of the results. Thus we further plan to investigate the more accurate modelling of our independent variable Total Medicare Standardized Payment Value using

dataset = pd.read_csv (‘dataset.csv’) // import dataset

// Independent values and dependent values are separated,

//x denotes independent variable and y will be the dependent variable

x=dataset.iloc[:,0:27].values

y=dataset.iloc[:,27].values

// Convert all dependent data into integer values

ConvertInteger(Dependent Data)

TestSet [] = dataset (20% randomly selected)

TrainingSet [] = dataset (80% randomly selected)

Standardize (dataset)

CreateHiddenLayers()

// 2-3 hidden layers are created with an output dimension of 13

//and input dimension of 30

set(X_train,Y_train, Batch and Epoch values),

// X-train is the training set of the independent variable (x) and

//Y_train is training set corresponding to dependent variable y

//The values used are Batch= 32 and Epoch = 100

do

{

Y_predict = classifier.predict (X_Test)

// The unlabeled observations (X_Test) used are 20% of the entire dataset

// the threshold value of 50% is set for predicted labels (y_predict).

} while (Epoch <=100);

GenerateConfusionMatrix ()

Figure 6. Algorithm for implementing the healthcare system using DLT.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 107

DLT algorithm. The simulation value gave a result of R2 as 0.5159, which in a way indicates the variance was reduced by 51%.

For the purpose of applying DLT the system is trained by randomly selecting 32,530 records (80%) and tested using 8133 records (20%). The above mentioned analysis methodology was out to test on the dataset mentioned in the introduction section. In addition, the LR model depicted in Figure 7 had a much lesser level of accuracy. The conceptual meaning of the Confusion Matrix (CM) for two-hidden layers, considering Outcome-1 (O1) is tabulated in Table 1.

The details of the CM illustrated in Table 1 are as follows:

● True Negative (TN) value = 4013 which indicates the values of the predicted output that is correctly considered as 0 as per the O1 (Result = 0 when Medicare Standardized Payment Received by a Physical Therapist is less than its median).

● True Positive (TP) value = 4066 which indicates the values of the predicted output that is correctly considered as 1 as per the O1 (Result = 1 when Medicare Standardized Payment Received by a Physical Therapist is greater than its median).

● False Negative (FN) value = 28 which indicates the values of the predicted output that is wrongly considered as 0 as per the O1 (Result = 0 when Medicare Standardized Payment Received by a Physical Therapist is less than its median).

● False Positive (FP) value = 26 which indicates the values of the predicted output that is wrongly considered as 0 as per the O1 (Result = 1 when Medicare Standardized Payment Received by a Physical Therapist is greater than its median).

Accordingly, (TN) 4013 + (TF) 4066 = 8079 matched correctly; (FN) 28 + (FP) 26 = 54 not matched (Table 1). Accuracy can be calculated as ¼ Data matched correctly

Total data = 8079/8133 = 99.33%. The concep- tual meaning of CM for three-hidden layers, considering O1 is tabulated in Table 2.

However, (TN) 4015 + (TP) 4080 = 8095 matched correctly; (FN) 14 + (FP) 24 = 38 not matched (Table 2). Accuracy can be calculated as ¼ Data matched correctly

Total data = 8095/8133 = 99.53%. The system is trained by randomly selecting 32,530 records (80%) and tested using 8,133

records (20%). The conceptual meaning of the CM for two-hidden layers, considering Outcome-2 (O2) is tabulated in Table 3. Additionally, the data generated for three-hidden layers considering O2 is presented in Table 4.

Figure 7. (a) Simple Linear Regression (LR) analysis, (b) Multiple LR analysis.

108 V. P. GURUPUR ET AL.

The CM given in Table 3 represents (TN) 6760 + (TF) 1339 = 8099 matched correctly; (FN) 9 + (FP) 27 = 36 not matched. Hence, the accuracy can be calculated as ¼ Data matched correctly

Total data = 8099/ 8133 = 99.58%. Further, the conceptual meaning of the CM for three-hidden layers, considering O2 is tabulated using Table 4. In which, (TN) 6741 + (TP) 1341 = 8082 matched correctly; whereas (FN)

5 + (FP) 27 = 32 not matched. In this case, accuracy can be calculated as ¼ Data matched correctly Total data

= 8082/8133 = 99.37%. Table 5 presents comprehensive summary of performance achieved for the set O1 and O2 for

the proposed Medicare analysis system. Therefore, it can be clearly identify that Deep Learning Technique (DLT) can perform automatic feature extraction which is not possible in Linear Regression (LR). The DLT network can automatically decide which characteristics of data can be used as indicators to label that data reliably. DLT has recently surpassed all the conventional Machine Learning (ML) techniques with minimal tuning and human effort. This effectively repre- sents the DLT network can automatically decide which characteristics of data can be used as indicators to label that data reliably.

The key observations of this experiment are as follows: (i) DLT has a better accuracy when compared to LR method for a single set of the variables, (ii) the accuracy of DLT increases exponentially (99.58%) when multiple dependent variables are considered, (iii) adding additional

Table 1. Confusion Matrix (CM) for two-hidden layers considering Outcome-1 (O1) .

O1 CM Two-hidden layers

PREDICTED

NO YES

ACTUAL NO TN = 4013 FP = 26 YES FN = 28 TP = 4066

Table 2. CM for three-hidden layers considering O1.

O1 CM Three-hidden layers

PREDICTED

NO YES

ACTUAL NO TN = 4015 FP = 24 YES FN = 14 TP = 4080

Table 3. CM for two-hidden layers considering O2.

O2 CM Two-hidden layers

PREDICTED

NO YES

ACTUAL NO TN = 6760 FP = 27 YES FN = 7 TP = 1339

Table 4. CM for three-hidden layers considering O2.

O2 CM Three-hidden layers

PREDICTED

NO YES

ACTUAL NO TN = 6741 FP = 46 YES FN = 5 TP = 1341

Table 5. Summary of accuracy obtained for O1 and O2 using two-layer and three-layer models.

Outcome Accuracy TPþTN TPþTNþFPþFN

O1 Two-hidden layers 99.34% Three-hidden layers 99.53%

O2 Two-hidden layers 99.58% Three-hidden layers 99.37%

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 109

hidden neural network layer for Outcome-2 (O2) did not increase the accuracy (99.37%) of prediction.

Comparison with techniques used in medical imaging

Zhang (Zhang et al., 2016) applied five-layer Deep DNN Support Vector Machine (SVM) to detect colorectal cancer and achieved with precision 87.3%, recall rate 85.9% and accuracy 85.9%. However, the method lacks in simultaneous detection as well as the classification of polyps. Furthermore, random background considered which may lead to increase in the False Positive (FP) rate (Zhang et al., 2016) (Yu, Chen, Dou, Qin, & Heng, 2017) for offline and online colorectal cancer prevention and diagnosis subjected the three-dimensional fully connected Convolutional Neural Network (CNN) and obtained precision of 88%, recall rate of 71%, F1 79% and F2 of 74%. In (Yu et al., 2017) study, it was observed that there is a high interclass relationship and intra class distinction regarding colon polyps. Here translation is difficult for machine learning algorithms to correctly classify the polyps. Christodoulidis (Christodoulidis, Anthimopoulos, Ebner, Christe, & Mougiakakou, 2017) conducted study to classify the inter- stitial lung disease using ensemble of multi-source transfer learning method. Here the investigators attained F-score of 88.17%. However in the developed technique the computa- tional complexity is more due to multilevel feature extraction measures. (Tan, Fujita, et al., 2017b) (Tan, Acharya, Bhandary, Chua, & Sivaprasad, 2017) identified diabetic retinopathy by constructing ten-layer CNN. Here the investigators observed a sensitivity of 87.58% for detection of exudates and sensitivity of 71.58% for dark lesions identification. Akkus (Akkus, Galimzianova, Hoogi, Rubin, & Erickson, 2017) investigated tumour genomic prediction using two-dimensional CNN and observed 93% of sensitivity, 82% of specificity, and 88% of accuracy. Furthermore, Kumar (Kumar, Kim, Lyndon, Fulham, & Feng, 2017) developed system for classification of modality of medical images and achieved accuracy of 96.59% using ensemble of fine-tuned CNN. It was observed that ensemble of CNNs will enable higher quality features to be extracted. Later, Lekadir (Lekadir et al., 2017) conducted study to characterise the plaque composition by applying nine-layers of CNN. In this technique 78.5% accuracy was evaluated, where the ground truth is verified by a single physician. Therefore, we can conclude that DLT used by the investigators in the study delineated in this article had a much higher degree of accuracy when it came to predictability.

Comparison with techniques used in pervasive sensing

Hannink (Hannink et al., 2017) developed system for mobile gait analysis considering DCNN. Here the authors reported precision of 0.13 ± 3.78°. However in (Hannink et al., 2017) parameter space such as number and dimensionality of kernels are not considered. Ravi (Ravi et al., 2017) designed methodology to recognise human activity applying DNN and achieved 95.8% of accuracy. This method demonstrates the feasibility of real-time investigation, however the computation cost obtained is significantly less. The results obtained in the technique employed by the investigators far exceeds this value as well.

Comparison with techniques used to analyse biomedical signals

The investigators have achieved a higher level of accuracy with respect to perceived analysis of biomedical signals. Acharya, Oh, et al., 2017 classified arrhythmic heartbeats subjecting nine- layer augmented data DCNN. Using this technique authors achieved augmented data accuracy of 94.03% and imbalanced data accuracy of 89.3%. In fact this method requires long training hours and the specialised hardware to train. Further, normal and Myocardial Infarction (MI) ECG beats were detected using CNN and the investigators for this study reported an accuracy of

110 V. P. GURUPUR ET AL.

93.53% with noise and 95.22% without noise (Acharya, Fujita, et al., 2017b). Later using same CNN architecture CAD beats were classified with accuracy of 95.11%, sensitivity of 91.13% and specificity of 95.88% (Acharya, Fujita, Lih, et al., 2017). Also studies were conducted using CNN model to detect tachycardia beats of five seconds duration and reported accuracy, sensitivity and specificity of 94.90%, 99.13% and 81.44%, respectively. However, in their technique few of the remarks were observed. Such as computationally difficult in learning the features, limited database is applied, training process requires huge database and tested using restricted dataset.

Comparison with techniques used in personalised healthcare

Pham (Pham, Tran, Phung, & Venkatesh, 2017) developed algorithm for Electronic Medical Records (EMRs) using deep dynamic memory NN. In this study the investigators achieved F-score of 79.0% and confidence interval of (77.2–80.9) %. This system is more suitable for long progresses of many incidences. However, the young patients normally have only one or two admissions. Also, Nguyen (Nguyen, Tran, Wickramasinghe, & Venkatesh, 2017) designed automated tool to predict the future risk constructing the CNN model. In which the AUC measured for 3 months was 0.8 and for 6 months it was 81.9%. It was noticed that accurate and exact risk estimation is an important step towards the personalised care. However, in the analysis illustrated in this article, we have used the secondary dataset to evaluate the effective- ness of DLT methods (Desai, Martis, Nayak, Sarika, & Seshikala, 2015). As mentioned before, this dataset was constructed based on the 2014 Medicare Provider Utilization and Payment Data: Physician and Other Supplier Public Use File (Medicare Provider and Utilization Data, Online 2018), which contains information on services provided to beneficiaries by 40,662 physical therapists (Liu, et al, 2018).

Limitations

The research delineated in this article suffers from the following limitations: (a) the computational techniques used requires a high performance for this purpose a sample derived using a rando- mised approach was used, and (b) the Deep Learning Technique has only been tested on the aforementioned 2014 Medicare Provider and Utilization Data, it has not yet been experimented on other data samples.

Conclusion

In this article we have successfully proved the power and accuracy of using DLT over traditional methods (Desai et al., 2016) (Liu, Oetjen, et al, unpublished) (Jain, Kumar, & Fernandes, 2017) (Desai et al., 2016) (Bokhari, Sharif, Yasmin, & Fernandes, 2018) (Desai et al., 2015) (Desai, et al., 2016) on analysing the healthcare data. Table 6 provides the detailed comparison on this statement. The core contribution of the research delineated in this article is the introduction of new mathematical techniques harnessing DLT. While dis- cussing the results we also proved that our technique had a much higher accuracy level than the techniques used in available literature in medical imaging, pervasive sensing, analysing biomedical signals, and personalised healthcare. Additionally, here we have fully illustrated the power of higher computational techniques over traditional methods. The future direction of research on this particular topic would be: (a) application of the deep learning methods addressed in this study, on other types of healthcare data (Desai et al., 2015) (Naqi, Sharif, Yasmin, & Fernandes, 2018) (Desai, Nayak, et al., 2017b) (Desai, Nayak, Seshikala, & Martis, 2017) (Shah, Chen, Sharif, Yasmin, & Fernandes, 2017) (LeCun, et al, 2015) (Swasthik & Desai, 2017), (b) further modification of the DLTs (Mehrtash et al., 2017) considered with the purpose of improvising it from a computational perspective (Gurupur & Gutierrez, 2016)

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 111

Ta bl e 6.

O ut lin e of

pr op

os ed

ap pr oa ch

an d ot he r m et ho

ds us in g D LT

an d M L te ch ni qu

es fo r di ff er en t ap pl ic at io ns .

Ap pl ic at io n

M et ho

d Re su lt

Co lo re ct al ca nc er

(p ol yp )

de te ct io n an d cl as si fi ca tio

n Tr an sf er

le ar ni ng

us in g SV M

fi ve -la ye r D CN

N an d no

n- lin ea r ac tiv at io n fu nc tio

n (Z ha ng

et al ., 20 17 )

Pr ec is io n – 87 .3 %

Re ca ll ra te

– 85 .9 %

an d

Ac cu ra cy

– 85 .9 %

O ffl in e an d on

lin e co lo re ct al ca nc er

pr ev en tio

n an d di ag no

si s

Sp at io -t em

po ra lf ea tu re s- 3- D Fu lly

CN N (Y u et

al ., 20 17 )

Pr ec is io n – 88 %

Re ca ll ra te

– 71 %

F1 – 79 %

an d F2

– 74 %

In te rs tit ia l

lu ng

di se as e cl as si fi ca tio

n En se m bl e of

m ul ti- so ur ce

tr an sf er

le ar ni ng

us in g an

au to m at ic m od

el se le ct io n (C hr is to do

ul id is et

al ., 20 17 )

Fs co re

– 88 .1 7%

D ia be tic

re tin

op at hy

Te n- la ye r CN

N (T an ,A

ch ar ya ,e t al ., 20 17 a)

Se ns iti vi ty

– 87 .5 8%

fo r ex ud

at es

an d Se ns iti vi ty

– 71 .5 8%

fo r da rk

le si on

s Tu m ou

r ge no

m ic pr ed ic tio

n 2- D pa tc h w is e CN

N (A kk us

et al ., 20 17 )

Se ns iti vi ty -9 3%

,S pe ci fi ci ty

– 82 %

an d Ac cu ra cy

– 88 %

Cl as si fi ca tio

n of

m od

al ity

of m ed ic al im ag es

En se m bl e of

fi ne -t un

ed CN

N (K um

ar et

al ., 20 17 )

Ac cu ra cy

– 96 .5 9%

Ch ar ac te ris at io n of

pl aq ue

co m po

si tio

n CN

N w ith

ni ne -la ye rs

(L ek ad ir et

al ., 20 17 )

Ac cu ra cy

– 78 .5 %

M ob

ile ga it an al ys is

D CN

N (H an ni nk

et al ., 20 17 )

M ea n 0. 15

� 6. 09

cm an d pr ec is io n 0. 13

� 3. 78

� H um

an Ac tiv ity

Re co gn

iti on

D N N (R av ì, W on

g, Lo ,&

Ya ng

,2 01 7)

Ac cu ra cy

– 95 .8 %

Ar rh yt hm

ic he ar tb ea ts sc re en in g

N in e- la ye r au gm

en te d da ta

D CN

N (A ch ar ya ,O

h, et

al ., 20 17 )

Au gm

en te d da ta

Ac cu ra cy

– 94 .0 3%

an d

im ba la nc ed

da ta

Ac cu ra cy

– 89 .3 %

D et ec tio

n of

M yo ca rd ia lI nf ar ct io n (M

I) CN

N (A ch ar ya

et al ., 20 17 d)

Ac cu ra cy

– 93 .5 3%

w ith

no is e an d 95 .2 2%

w ith

ou t no

is e

D et ec tio

n of

Co ro na ry

Ar te ry

D is ea se

(C AD

) El ev en -la ye rs CN

N (A ch ar ya

et al ., 20 17 a)

Ac cu ra cy

− 95 .1 1%

,S en si tiv ity

– 91 .1 3%

an d Sp ec ifi ci ty

– 95 .8 8%

D et ec tio

n of

ta ch yc ar di a

El ev en -la ye rs CN

N (A ch ar ya

et al ., 20 17 c)

Ac cu ra cy

– 94 .9 0%

Se ns iti vi ty

– 99 .1 3%

,a nd

Sp ec ifi ci ty

– 81 .4 4%

El ec tr on

ic M ed ic al Re co rd s (E M Rs )

D ee p dy na m ic m em

or y N N (P ha m

et al ., 20 17 )

F- sc or e – 79 .0 %

Co nfi

de nc e In te rv al – (7 7. 2– 80 .9 )%

M ed

ic ar e da

ta ut ili sa ti on

sy st em

D ee p Le ar ni ng

Te ch ni qu

e (D LT ) (p ro po

se d ap

pr oa

ch )

A cc ur ac y – 99

.5 3%

fo r Ou tc om

e – 1( O 1)

an d 99

.5 8 fo r

Ou tc om

e – 2 (O 2)

112 V. P. GURUPUR ET AL.

(Nasir, Liu, Gurupur, & Qureshi, 2017) (Gurupur & Tanik, 2012) (Gurupur, Sakoglu, Jain, & Tanik, 2014) (Desai, et al., 2018). This improvisation is because of the fact that a high performance computational facility is required to carry out the computer programme in the implementa- tion system.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Usha Desai http://orcid.org/0000-0002-2267-2567

References

Acharya, U. R., Fujita, H., Lih, O. S., Adam, M., Tan, J. H., & Chua, C. K. (2017). Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems. doi:10.1016/j.knosys.2017.06.003

Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences. doi:10.1016/j.ins.2017.04.012

Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences. doi:10.1016/j. ins.2017.06.027

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., & San, T. R. (2017). A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine. doi:10.1016/j.compbiomed.2017.08.022

Akkus, Z, Galimzianova, A, Hoogi, A, Rubin, D. L, & Erickson, B. J. (2017). Deep learning for brain mri segmentation: state of the art and future directions. Journal Of Digital Imaging, 30(4), 449-459. doi:10.1007/s10278-017-9983-4

Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017). Deep learning for brain MRI segmentation: State of the art and future directions. Journal of Digital Imaging. doi:10.1007/s10278-017-9983-4

Bokhari, S. T. F., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018). Fundus image segmentation and feature extraction for the detection of glaucoma: A new approach. Current Medical Imaging Reviews. doi:10.2174/ 1573405613666170405145913

Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., & Mougiakakou, S. (2017). Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE Journal of Biomedical and Health Informatics, 21 (1), 76–84.

Desai U. et al. (2015) Discrete Cosine Transform Features in Automated Classification of Cardiac Arrhythmia Beats. In: Shetty N., Prasad N., Nalini N. (eds) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi.

Desai, U., Martis, R. J., Acharya, U. R., Nayak, C. G., Seshikala, G., & Shetty, R. K. (2016). Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. Journal of Mechanics in Medicine and Biology, 16, 1640005.

Desai, U., Martis, R. J., Nayak, C. G., Sarika, K., & Seshikala, G. (2015). Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques, India Conference (INDICON), Proceedings of the annual IEEE India conference, doi: 10.1109/INDICON.2015.7443220

Desai, U., Martis, R. J., Nayak, C. G., Sheshikala, G., Sarika, K., & Shetty, R. K. (2016). Decision support system for arrhythmia beats using ECG signals with DCT, DWT and EMD methods: A comparative study. Journal of Mechanics in Medicine and Biology, 16, 1640012.

Desai, U., Nayak, C. G., & Seshikala, G. An application of EMD technique in detection of tachycardia beats. In Communication and Signal Processing (ICCSP), 2016 International Conference on 2016 Apr 6 (pp. 1420–1424). IEEE.

Desai, U., Nayak, C. G., & Seshikala, G. An efficient technique for automated diagnosis of cardiac rhythms using electrocardiogram. In Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on 2016 May 20 (pp. 5–8), Bengaluru, India. IEEE. DOI:10.1109/RTEICT.2016.7807770.

Desai, U., Nayak, C. G., & Seshikala, G. (2017). Application of ensemble classifiers in accurate diagnosis of myocardial ischemia conditions. Progress in Artificial Intelligence, 6(3), 245–253.

Desai, U., Nayak, C. G., Seshikala, G., & Martis, R. J. (2017). Automated diagnosis of coronary artery disease using pattern recognition approach. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 434–437.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 113

Desai, U., Nayak, C.G., Seshikala, G., Martis, R.J., & Fernandes, S.L. (2018). Automated Diagnosis Of Tachycardia Beats. In: Satapathy S., Bhateja V., Das S. (eds) Smart Computing and Informatics. Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. doi:https://doi.org/10.1007/978-981-10-5544-7_41

Diehr, P., Yanez, D., Ash, A., Hornbrook, M., & Lin, D. Y. (1999). Methods for analyzing healthcare utilization and costs. Annual Review of Public Health, 20, 125–144.

Fernandes, S. L., Chakraborty, B., Gurupur, V. P., & Prabhu, A. (2016). Early skin cancer detection using computer aided diagnosis techniques. Journal of Integrated Design and Process Science, 20(1), 33–43.

Fernandes, S. L., Gurupur, V. P., Lin, H., & Martis, R. J. (2017). A novel fusion approach for early lung cancer detection using computer aided diagnosis techniques. Journal of Medical Imaging and Health Informatics, 7(8), 1841–1850.

Fernandes, S. L., Gurupur, V. P., Sunder, N. R., & Kadry, S. (2017). A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognition Letters, 94(15), 87–95.

Gurupur, V., & Gutierrez, R. (2016). Designing the right framework for healthcare decision support. Journal of Integrated Design and Process Science, 20, 7–32.

Gurupur, V., Sakoglu, U., Jain, G. P., & Tanik, U. J. (2014). Semantic requirements sharing approach to develop software systems using concept maps and information entropy: A personal health information system example. Advances in Engineering Software, 70, 25–35.

Gurupur, V., & Tanik, M. M. (2012). A system for building clinical research applications using semantic web-based approach. Journal of Medical Systems, 36(1), 53–59.

Hannink, J., Kautz, T., Pasluosta, C. F., Gaßmann, K. G., Klucken, J., & Eskofier, B. M. (2017). Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 21(1), 85–93.

Hempelmann, C. F., Sakoglu, U., Gurupur, V., & Jampana, S. (2015). An entropy-based evaluation method for knowl- edge bases of medical information systems. Expert Systems with Applications, 46, 262–273.

Jain, V. K., Kumar, S., & Fernandes, S. L. (2017). Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. Journal of Computational Science, 21, 316–326.

Khan, M. W., Sharif, M., Yasmin, M., & Fernandes, S. L. (2016). A new approach of cup to disk ratio based glaucoma detection using fundus images. Journal of Integrated Design and Process Science, 20(1), 77–94.

Kim, H.-Y. (2014). Analysis of Variance (ANOVA) comparing means of more than two groups. Restorative Dentistry and Endodontics, 39(1), 74–77.

Kulkarni, S. A., & Rao, G. R. (2009). Modeling reinforcement learning algorithms for performance analysis. In Proceedings of ICAC3ʹ09 of the International Conference on Advances in Computing, Communication and Control (pp. 35–39), Mumbai, India. doi:10.1145/1523103.1523111.

Kumar, A., Kim, J., Lyndon, D., Fulham, M., & Feng, D. (2017). An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE Journal of Biomedical and Health Informatics, 21(1), 31–40.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. Lekadir, K., Galimzianova, A., Betriu, À., del Mar Vila, M., Igual, L., Rubin, D. L., . . . Napel, S. (2017). A convolutional neural

network for automatic characterization of plaque composition in carotid ultrasound. IEEE Journal of Biomedical and Health Informatics, 21(1), 48–55.

Liu, X., Oetjen, R. M., Hanney, W. J., Rovito, M., Masaracchio, M., Peterson, R. L., & Dottore, K. (2018). Characteristics of physical therapists serving medicare fee-for-service beneficiaries (Unpublished manuscript).

Malehi, A. S., Pourmotahari, F., & Angali, K. A. (2015). Statistical models for the analysis of skewed healthcare cost data: A simulation study. Health Economics Review, 5. doi:10.1186/s13561-015-0045-7

Martis, R. J., Lin, H., Gurupur, V. P., & Fernandes, S. L. (2017). Editorial: Frontiers in development of intelligent applications for medical imaging processing and computer vision. Computers in Biology and Medicine, 89, 549–550.

Medicare Provider Utilization and Payment Data: Physician and Other Supplier. (2018, February 26). [Online]. Retrieved from https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider- Charge-Data/Physician-and-Other-Supplier.html

Mehrtash, A., Sedghi, A., Ghafoorian, M., Taghipour, M., Tempany, C. M., Wells, W. M., . . . Fedorov, A. (2017). Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks. Proceedings of SPIE–the international society for optical engineering, Orlando, Florida, United States. doi: 10.1117/ 12.2277123.

Naqi, S. M., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018). Lung nodule detection using polygon approximation and hybrid features from lung CT images. Current Medical Imaging Reviews. doi:10.2174/1573405613666170306114320

Nasir, A., Liu, X., Gurupur, V., & Qureshi, Z. (2017). Disparities in patient record completeness with respect to the health care utilization project. Health Informatics Journal. doi:10.1177/1460458217716005

Nguyen, P., Tran, T., Wickramasinghe, N., & Venkatesh, S. (2017). Deepr: A convolutional net for medical records. IEEE Journal of Biomedical and Health Informatics, 21(1), 22–30.

Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression. Organizational Research Methods, 16(4), 650–674.

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017). Predicting healthcare trajectories from medical records: A deep learning approach. Journal of Biomedical Informatics, 69, 218–229.

114 V. P. GURUPUR ET AL.

Puppala, M., He, T., Chen, S., Ogunti, R., Yu, X., Li, F., . . . Wong, S. T. C. (2015). METEOR: An enterprise health informatics environment to support evidence-based medicine. IEEE Transactions on Biomedical Engineering, 62(12), 2776–2786.

Rajinikanth, V., Satapathy, S. C., Fernandes, S. L., & Nachiappan, S. (2017). Entropy based segmentation of tumor from brain MR images - A study with teaching learning based optimization. Pattern Recognition Letters, 94, 87–95.

Ravì, D., Wong, C., Lo, B., & Yang, G. Z. (2017). A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE Journal of Biomedical and Health Informatics, 21(1), 56–64.

Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21.

Santana, D. B., Z´Ocalo, Y. A., Ventura, I. F., Arrosa, J. F. T., Florio, L., Lluberas, R., & Armentano, R. L. (2012). Health informatics design for assisted diagnosis of subclinical atherosclerosis, structural, and functional arterial age calculus and patient-specific cardiovascular risk evaluation. IEEE Transactions on Information Technology in Biomedicine, 16(5), 943–951.

Shabbira, B., Sharifa, M., Nisara, W., Yasmina, M., & Fernandes, S. L. (2017). Automatic cotton wool spots extraction in retinal images using texture segmentation and Gabor wavelet. Journal of Integrated Design and Process Science, 20 (1), 65–76.

Shah, J. H., Chen, Z., Sharif, M., Yasmin, M., & Fernandes, S. L. (2017). A novel biomechanics based approach for person re-identification by generating dense color sift salience features. Journal of Mechanics in Medicine and Biology, 17, 1740011.

Snee, N. L., & McCormick, K. A. (2004). The case for integrating public health informatics networks. IEEE Engineering in Medicine and Biology Magazine, 23(1), 81-88.

Suinesiaputra, A., Gracia, P. P. M., Cowan, B. R., & Young, A. A. (2015). Big heart data: Advancing health informatics through data sharing in cardiovascular imaging. IEEE Journal of Biomedical and Health Informatics, 19(4), 1283–1290.

Swasthi, D. U. (2017). Automated detection of cardiac health condition using linear techniques. In Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017 2nd IEEE International Conference on 2017 May 19 (pp. 890–894). IEEE.

Tan, J. H., Acharya, U. R., Bhandary, S. V., Chua, K. C., & Sivaprasad, S. (2017a). Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. Journal of Computational Science. doi:10.1016/j. jocs.2017.02.006

Tan, J. H, Fujita, H, Sivaprasad, S, Bhandary, S. V, Rao, A. K, Chua, K. C, & Acharya, U. R. (2017b). Automated segmentation of exudates, hemorrhages, microaneurysms using single convolutional neural network. In Information sciences, 420(c) (pp. 66–76).

Taylor, J. G. (Eds). (1993). Mathematical approaches to neural networks (Vol. 51, 1st ed.). North Holland: Elsevier. The Centers for Medicare and Medicaid Services, Office of Enterprise Data and Analytics. (2016). Medicare fee-for-

service provider utilization & payment data physician and other supplier public use file: A methodological over- view. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/ Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html

Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability and statistics for engineers and scientists (9th ed., pp. 361–363). Boston, USA: Prentice Hall.

Weitzel, M., Smith, A., Deugd, S., & Yates, R. (2010). A web 2.0 model for patient-centered health informatics applications. Computer, 43(7), 43–50.

Xu, L.-W. (2014). MANOVA for nested designs with unequal cell sizes and unequal cell covariance matrices. Journal of Applied Mathematics. doi:10.1155/201/649202.2014

Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P. A. (2017). Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE Journal of Biomedical and Health Informatics, 21(1), 65–75.

Zhang, R., Zheng, Y., Mak, T. W., Yu, R., Wong, S. H., Lau, J. Y., & Poon, C. C. (2016). Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE Journal of Biomedical and Health Informatics, 21(1), 41–47.

Zhang, R, Zheng, Y, Mak, Tony Wing Chung, Yu, R, Wong, SH, Lau, James Y. W, & Poon, Carmen C. Y. (2017). Automatic detection and classification of colorectal polyps by transferring low-level cnn features from nonmedical domain. Ieee Journal Of Biomedical and Health Informatics, 21(1), 41-47. doi:10.1109/JBHI.2016.2635662

Zhang, Y.-T., Zheng, Y.-L., Lin, W.-H., Zhang, H.-Y., & Zhou, X.-L. (2013). Challenges and opportunities in cardiovascular health informatics. IEEE Transactions on Biomedical Engineering, 60(3), 633–642.

Zheng, Y.-L., Ding, X.-R., Poon, C. C. Y., Lo, B. P. L. H., Zhang, X.-L., Zhou, G.-Z., . . . Zhang, Y.-T. (2014). Unobtrusive sensing and wearable devices for health informatics. IEEE Transactions on Biomedical Engineering, 61(5), 1538–1554.

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 115

Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.