Predictive analytics to prevent and control chronic diseases

In these days, chronic diseases are the imperative reason for death in the world. Therefore, there is a noteworthy increment in consideration being paid to individual wellness as a preventative methodology in healthcare. However, creating and building a prediction model for chronic diseases is an extraordinary change to healthcare technology on the premise of data-analysis and decision-making level. In this paper, effective mechanisms have been used for chronic disease prediction by mining the data containing historical health records. Here, we used Naïve Bayes, Decision tree, Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classifiers for the diagnosis of diabetes and heart disease. In this study, we also present comparative study of different classifiers to measure the performance based on accuracy rate.