Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Data mining now-a-days plays a dynamic role in prediction of Disease. Data Mining is frequently defined as the process of determining patterns, correlations, trends or relationships by searching through a huge amount of data stored in repositories, databases, and data warehouses. Diabetes mellitus is an enduring disease and a foremost public health challenge allover. Diabetes affected over 246 million people worldwide with a common of them being women. According to the WHO report, by 2025 this number is projected to increase to over 380 million. This paper provides a survey and analysis of data mining methods that have been commonly applied to diabetes data analysis and prediction of the disease.

Keywords: Data mining, Diabetes mellitus, Classification, C4.5, Naïve Bayes, CART, Bayes Network, Pima Indian Diabetes Data(PIDD)