Prediction of Heart Diseases in Diabetic Patients

Abstract

In today’s world we observe that a large group of people are affected with diseases like Diabetes, Cancer, Heart related issues, etc., due to which a substantial amount of raw data is being collected in the medical industry. This paper aim’s to convert the obtained raw data into structured data using machine learning techniques in order to predict heart disease in diabetic patients. An Automated intelligent system to predict heart disease is developed using various data mining techniques such as Decision Tree, Logistic Regression, K nearest neighbor(Knn), Hybrid Algorithm and Random Forest. These data mining techniques on implementation provides a higher accuracy rate which is the primary requirement for better and faster prediction of heart disease in diabetic patients. The datasets used is obtained from PIDD (Pima Indians Diabetes Database) containing a number of instances with a set of attributes such as age, sex, blood sugar level, cholesterol etc. This system is developed to be User friendly by reducing the possibility of human errors and the time consumption.