PREDICTION OF TYPE 2 DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUES

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Among those critical diseases, Diabetes Mellitus is one of the chronic diseases which affect human well-being at a young stage. The chronic metabolic disorder diabetes mellitus is a rapidly growing global challenge imposing massive socio-economic and health hazards. It has been estimated that by the year 2020 there are nearly 285 million people (close to 6.4% of the adult age group) who are affected by this disease. This number has been estimated to rise to 430 million with no better control or treatment available. This rise in the rates in developing countries adopts the trend changes in urbanization and lifestyle, which includes a "western-style" diet also. This is due to the awareness being low . An aging population and obesity constitute are the primary reasons for the rise. In order to examine the high-risk population group of Diabetes Mellitus (DM), modern information technology has to be used. Data mining also called Knowledge Discovery in Databases (KDD) is defined to be the computational process of finding the patterns in massive datasets that include techniques intersecting Artificial Intelligence, Machine Learning, Statistics, and Database Systems. The important objectives of these techniques include Pattern Identification, Prediction, Association, and Clustering. Data mining consists of a set of steps executed either automatically or semi-automatically for extracting and finding intriguing, unknown, unseen features from a paramount volume of data. The superior quality of data and the rightly used technique are the two important concepts of data mining principle. Several computational approaches have been designed for the classification of diabetes occurs in humans. The usage of Machine Learning in the medical information system has been found to be advantageous since it improves the diagnostic accuracy, minimizes the expenditure, and also increases the number of treatments that have been successful for diabetes mellitus . For the automation of the overall process of diabetes prediction and severity estimation, a diabetic database is required. This archive of the diabetic database aids in identifying the effect of diabetes on different human organs.