Prediction and Analysis of Heart Disease Using SVM Algorithm

Abstract

In this paper the proposed system predicts Heart Disease using SVM algorithm. Heart disease is the most common cause of death globally. Heart disease prediction using data mining is one of the most interesting and challenging tasks. The shortage of specialists and high wrongly diagnosed cases has necessitated the need to develop a fast and efficient detection system. According to past system the integration of clinical decision support with computer based patient record can reduce medical errors, can be made more precise and hence enhance patient safety. The system helps in prediction of heart disease by considering risky factor associated with heart disease. Here system applies support vector machine algorithm on historical information/data of patient and it provides features like Age, Sex, Smoking, Overweight, Alcohol Intake, Bad Cholesterol ,Blood Pressure and Heart Rate to make prediction of coronary heart disease with higher accuracy.

Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases

“…Finally, classifier performance has been evaluated. In the current research work, heart diseases have been identified based on the discussed process, depending on the historical dataset of patients and support vector machine classifier (Patil, 2019). The sequential steps followed for evaluation of the kernel parameters of the proposed prediction system have been presented in Fig.…”