A Novel Approach For Prediction Of Diabetes Using Genetic Algorithm And Entropy Technique

The age-old adage, “Prevention is better than Cure,” underscores the significance of early disease detection. Diabetes, a burgeoning global health concern, ranks as a leading cause of mortality and is poised to escalate in prevalence. Proactive measures are vital to curb its impact. Anticipating the risk of diabetes in future generations can facilitate early preventive actions. This paper introduces an innovative algorithm that harnesses Data Mining techniques and Genetic Algorithms for diabetes prediction. Data mining plays a pivotal role in scrutinizing the data for diabetes prognosis, enabling the reduction of unnecessary tests. The proposed algorithm streamlines the diagnostic process by trimming down the requisite attributes for prediction by using entropy technique. This not only curtails costs but also yields highly accurate predictions of 96%. By embracing this cutting-edge approach, we can revolutionize disease detection, safeguarding the health of future generations and promoting a healthier society.