PDD: Predictive Diabetes Diagnosis using Data mining Algorithms

Data analytics is used to obtain useful insights from small or large data set to conclude some useful information and also used for future recommendation and decision making. Predictive Analytics uses data mining, machine learning techniques to make predictions about the future. It involves the analysis of available data. The predictive analytics in health care is primarily used to determine patients having initial stages of diabetes, asthma, heart disease, and another critical lifetime disease. 

The proposed method of PDD uses data mining algorithms to predict type2 diabetes. The data mining algorithms used in the proposed system are K-Means Clustering and Random Forest. The predictive model, PDD provides better results in terms of accuracy when compared to hierarchical clustering and Bayesian network clustering with random forest prediction.

Keywords: Type 2 diabetes, Prediction, K-Means Clustering, Random Forest