A Predictive Approach for Diabetes Mellitus Disease through Data Mining Technologies

Abstract

This study addresses for applying data-mining techniques in diabetes research which gives a rational insight to model predicate patterns that can forecast incidence of Diabetes Mellitus disease (DMD) in human race. Clinical Patient records and Pathological test reports inherently represent data sets which may be applied to data mining for diabetes research. Hidden knowledge rules may be extracted to new hypothesis for improving standards and quality in the field of health care for diabetes patients. Primary Data mining methods such as Rule classification and Decision trees are used.