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Application of Data mining in healthcare: A survey
Article in Asian Journal of Microbiology, Biotechnology and Environmental Sciences · December 2016
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Asian Jr. of Microbiol. Biotech. Env. Sc. Vol. 18, No. (4) : 2016 : 999-1001 © Global Science Publications ISSN-0972-3005
APPLICATION OF DATA MINING IN HEALTHCARE: A SURVEY
E. MERCY BEULAH 1, S. NIRMALA SUGIRTHA RAJINI 2 and N. RAJKUMAR3
Department of Computer Applications, Dr. M.G.R. Educational and Research Institute, Chennai, India
(Received 15 June, 2016; accepted 22 August, 2016)
Key words : Data Mining, Healthcare, Medical database, Healthcare services.
Abstract - Data Mining is one of the foremost motivating spaces for analysis that is mounting progressively standard in the healthcare industry. Data mining plays an efficient role in revealing the new emerging trends associated with this scenario. In the health industry, data processing provides many advantages in transactional applications like Electronic Health Record (EHR), patient satisfaction systems, lab systems, economic systems, patient identification etc. This survey highlights few applications and future issues of Data mining in medical field. It also provides a picture of a database which exists in health care organization.
*Corresponding author’s email : (1,3Assistant Professor, 2Associate Professor)
INTRODUCTION
The growth of information technology has generated a great deal of databases and large data in numerous areas. The issue of health care believes to be the prime importance for the society and is a significant indicator of social development. In the present era, Data Mining is becoming popular in the healthcare field because there is a need of efficient analytical methodology for finding unknown and valuable information in health data (Divya Tomar et al., 2013). The delivery of health care services thus assumes the greater proportion, and in this context the role played by information and communication techniques has certainly a greater input for its effective delivery mechanism.
The purpose of data mining is specifically relevant and it has been successfully applied in medical needs for its dependable precision accuracy and expeditious beneficial results (Manaswini Pradhan, 2014).
The data generated by the health organizations are very vast and complex due to which it is difficult to analyze the data in order to make important decisions regarding patient health. This data contains details regarding hospitals, patients, medical claims, treatment cost, medicine names,
lab details, phisican details etc. So, there is a need to generate an important tool for analyzing and extracting information from this complex healthcare data. The analysis of health data improves the healthcare of the patients by enhancing the performance of patient management tasks. The outcome of Data Mining technologies is to provide profit to healthcare organization for clustering the patients having similar type of illness or health issues so that healthcare organization provides them efficient treatments. The patient’s length of stay in hospital can be predicted and be planned for effective information system management. With recent technologies used in the medical field, enhances the medical services in a cost effective manner. In analyzing the various factors that are responsible for diseases, for example, food habits, different working environ- ment, and education level, living conditions, availability of pure water, healthcare services, environmental and agricultural factors data mining techniques are used.
Data Mining In Healthcare
Data Mining came into existence in the middle of 1990’s and appeared as a powerful tool that is suitable for fetching previously unknown pattern
1000 MERCY BEULAH ET AL.
and useful information from huge dataset. Various studies highlighted that data mining techniques helps the data possessor to analyze and discover unsuspected relationships among their data which in turn helpful in making decision. Data mining techniques have been used intensively and extensively by many organizations. In healthcare, data mining is gradually increasing popularity, if not by any case, becoming increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry (Manaswini Pradhan, 2014). Healthcare industry today generates large amounts of complex data about patients, hospital resources, disease diagno- sis, electronic patient records, medical devices etc. The integrated medical database gives a clear picture of the entire data that exist in any medical information system.
The large amount of data is a key resource for processing and analyzing of knowledge extraction that enables support for cost-savings and decision making. Data mining brings a set of tools and techniques that can be applied to this processed data to discover hidden patterns that provide healthcare professionals an additional source of knowledge for making decisions. Data mining is a collection of algorithmic ways to extract informative patterns from raw data. Data mining is purely data-driven; this feature is significant in health care (Nirmala et al., 2015).
Data mining can help Healthcare insurer detect fraud and abuse, Healthcare organizations to make customer relationship management decisions, Physicians to identify effective treatments and best practices, and for patients to receive better and more affordable healthcare services.
DATA MINING APPLICATIONS IN HEALTHCARE
Key Dimensions in Healthcare Management is Diagnosis and Treatment, Healthcare Resource Management, Customer Relationship Management and Fraud and Anomaly Detection (Prasanna Desikan et al., 2011).
Diagnosis and Treatment
Ultrasound images supports examining of tumor response to chemotherapy by Computer-assisted texture analysis (Hub, 2000). Using data mining techniques with magnetic resonance spectroscopy diagnosis the presence of brain neoplasm (Zellner, 2004). To identify and quantify senile plagues,
analysis of digital images of tissue sections are used in evaluating the severity of Alzheimer ’s disease. Data mining could be particularly useful in medicine when there is no dispositive support favoring a particular healing option. Based on patients’ profile, history, physical examination, diagnosis and utilize previous treatment patterns, new treatment plans can be effectively recom- mended.
Healthcare Resource Management
Using logistic regression models, hospital profiles based on risk-adjusted death with 30 days of non- cardiac surgery are compared. Neural network system is used to predict the disposition in children presenting to the emergency room with bronchiolitis. Effectively control the resource allocation by classifying high risk areas and predicting the need and usage of various resources. If the inpatient length of stay (LOS) can be predicted efficiently, the planning and management of hospital resources can be greatly enhanced. Fitness report and demographic details of patients is additionaly helpful for utilizing the market hospital resources effectively.
Customer Relationship Management
CRM is built on a mixed view of the customer across the whole association (Puschmann et al., 2011). The view of the customers is fractured about an enterprise; the view of the enterprise is a splintered view of the customer. Kohli et al.,????? demonstrate a web-based Physician Profiling System (PPS) to build up relationships with physicians and improve hospital profitability and quality.
Some demographic characteristics and institu- tional characteristics consistently have a signi- ficant effect on a patient’s satisfaction scores. Chronic illnesses (e.g. diabetes and asthma) require self management and a collaborative patient- physician relationship. The principles of applying data mining for customer relationship management also applicable to the healthcare industry. The detection of usage and purchase patterns and the eventual satisfaction can be used to improve overall customer satisfaction. Patients, pharma- cists, physicians and clinics are the customers. Prediction of purchasing and usage behavior can help to provide proactive ideas to reduce the overall cost and improve customer satisfaction.
1001Application of Data Mining in Healthcare: A Survey
Fraud and Anomaly Detection
The avoidance and early detection of medical insurance fraud, data mining has been used very successfully. The ability to detect strange behavior based on purchase, usage and other transaction behavior information has made data mining a key tool in a variety of organizations to detect fraudulent claims, wrong prescriptions and other abnormal behavior patterns.
The various frauds in healthcare industry can be listed as: Prescription fraud (claims for patients who do not exist ), Upcoding(claims for a medical procedure which is more expensive or not performed). The various fraud detection methods are Neural networks, genetic algorithms and nearest neighbor methods.
CONCLUSION
Data mining applications can more benefit all parties involved in the healthcare industry. The integrated healthcare information system will have
Fig. 1 Integrated Medical Database
the facility for finding the patient location based and suggest the nearest emergency center, arrange all necessary arrangements to be ready for the help to provide proactive initiatives to reduce the overall cost and increase customer satisfaction.
REFERENCES
Divya Tomar and Sonali Agarwal 2013. A survey on Data Mining approaches for Healthcare, International Journal of Bio Science and BioTechnology.
5 (5): 241-266.
Manaswini Pradhan 2014. Data Mining and Health Care: Techniques of Application. ISOI Journal of Engineering and Computer Science. 1 (1): 18-26.
Nirmala Sugirtha Rajini,S 2015. Access Control in Healthcare Information Management Systems Using Biometric Authentication, International Journal of Applied Environmental Sciences. 10 (1) : 143- 148.
Prasanna Desikan, Kuo-Wei Hsu and Jaideep Srivastava 2011. Data Mining for Healthcare Management, International conference on Data Mining, Arizona, USA.
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