Diabetes Malady Prediction Using Data Mining Algorithms

Abstract

Diabetes is considered as one of the world's most prevalent incurable diseases.422 million individuals around the world are affected by incurable diabetes malady is reported by the World Health Organization. Therapy can be amplified by anticipating diabetes malady at an early echelon. The techniques conversant to data mining are used extensively to anticipate diabetes at a preliminary phase. Individual’s chances of having a diabetes malady can be anticipated by some momentous attributes that are playing a crucial preamble to enumerate diabetes malady at an early echelon. Symptoms data of the forthwith diabetes malady invaded people or no diabetes invaded people have been used to predict the diabetes disease at an early period in this diabetes malady anticipating work. On the diabetes malady anticipation, dataset multiple data mining algorithms such as Random Forest (RF), Decision Tree, Extra Trees, XGBoost, and Bagging have been implemented. Data mining algorithms accuracy has been assimilated in this diabetes malady prediction work. Among the mentioned data mining algorithms the Random Forest provided the best accuracy while using the Percentage split technique. Ultimately, it can be said that to aid the diabetes malady anticipation operation the Random Forest is more suitable for this research work than the others algorithms which are used in this work.