Prediction of Heart Problems for Diabetic Patients using Classification Algorithms

Srinivasu Badugu

Prolonged diabetes patients, without proper control are having higher risk factors for many diseases. The major problems are heart disease, kidney disease (renal), eye disorder, etc. Many classification techniques are available for the prediction of heart problems. But for the diabetes patients, the existing dataset are not sufficient for prediction of the same. In this work, the authors gathered information about the 2 different dataset; the first one is for the prediction of heart disease and the second one is for the prediction of diabetes. Also authors created a dataset of diabetic patients suffering with and without heart disease. The dataset consists of 500 records with 13 attributes and class HD (heart problem is there or not). In preprocessing stage, dataset were normalized using Min-Max & Zscore techniques. By using python programming language, Models were built with the help of two classification algorithms, namely SVM (Support vector machine) and KNN (K nearest neighbors). Among these 2 models, SVM gives higher accuracy rate in predicting the heart problems.

Volume 12 | 02-Special Issue

Pages: 904-913

DOI: 10.5373/JARDCS/V12SP2/SP20201148