INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

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ANALYSIS OF DATA MINING CLASSIFICATION BASED ALGORITHMS IN HEALTH CARE SECTOR

    2 Author(s):  DR. MRS. Y. V. BHAPKAR,DR. A. B. NIMBALKAR

Vol -  9, Issue- 3 ,         Page(s) : 6 - 13  (2019 ) DOI : https://doi.org/10.32804/RJSET

Abstract

Data Mining is a way towards analyzing data findings covered up or obscure examples in extremely large datasets that are possibly helpful and logical. The objective of data mining is to extract meaningful data from tremendous informational repositories. Data mining provides different views and summary operations into useful information. Data mining is the process of discovering hidden or unknown patterns from the huge datasets , these patterns are potentially valuable and eventually understandable. The goal of data mining is to develop an understandable and structured model by applying different data mining techniques for future use. These techniques are based on statistics, machine learning and database management theory. Data mining plays important role in all the domains including science, commerce, health care industries, marketing, banking, telecommunication, government organizations, agriculture, educational sectors, weather forecasting , web applications and many more. This study is based on data mining case study in health care sector. In this sector data mining plays important role to predict a disease at early stage for future diagnosis. The main objective of this study is to predict diabetes depending on few given attributes. Diabetes is a unceasing disease caused due to the increased level of sugar in the blood. Various automated information systems were developed which utilizes the various classifiers to anticipate and diagnose the diabetes. In this case body does not properly process food for use as energy. The pancreas, make a hormone called insulin to help glucose get into the cell of our bodies. If diabetes is not processed and disclosed at earlier stage then many complications may occur. Early diagnosis can save individual’s life and can manage over the diseases. Diabetics identifying processes results in visiting of a patient to a diagnostic center and consulting doctor. Using machine learning approaches we may solves this problem. The motive of this study is to design a model which can predict the likelihood of diabetes in various patients with maximum accuracy. We have proposed the use of Naïve Bayes,J48 ,Random forest and Multi layer perseptron classifiers for developing diabetics detection models .And then compared the models for the best accuracy.

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