Study on Data Mining Techniques for Diabetes Mellitus

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Data mining is an analytic process designed to explore large amount of data in search of consistent patterns and systematic relationships between variables and then to validate the findings by applying the detected patterns to new sub sets of data. The ultimate goal of data mining is prediction and predictive data mining is the most common type of data mining. The process of data mining consists of three stages 1) Initial exploration 2) Model building and 3) Deployment i.e., the application of the model to new data in order to generate prediction. In this paper, we discovered the unseen but important risk factors of diabetes and their relationships. We used the above mentioned three stages of data mining to identify how different risk factors contribute towards diabetes and how diabetes contribute to other diseases.

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Keywords

Attribute Reduction, Decision tree induction, Gini index.