Analysis and Comparison of Prediction of Heart Disease Using Novel Support Vector Machine and Logistic Regression Algorithm

G. Pavithraa¹, Sivaprasad¹

¹ Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, India

Abstract

Aim : prediction of coronary disease using novel support vector machine and comparing its accuracy with logistic regression algorithm. Materials and methods : Two social affairs are proposed for predicting the accuracy( %) of coronary disease. To be explicit, the novel supports vector machine and logistic regression algorithms. Here we take 20 samples each for appraisal and compare. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The logistic regression gives better precision (87.82%) than the novel support vector machine(SVM) accuracy (81.30%). Thus the real significance of logistic regression is better than novel support vector machine algorithms. Conclusion: From the result, it might be gathered that logistic regression helps in expecting the coronary sickness with more accuracy to appear differently in relation to novel support vector machine algorithms.

Imprint

G. Pavithraa, Sivaprasad. Analysis and Comparison of Prediction of Heart Disease Using Novel Support Vector Machine and Logistic Regression Algorithm. Cardiometry; Issue 25; December 2022; p.783-787; DOI: 10.18137/cardiometry.2022.25.783787; Available from: https://www.cardiometry.net/issues/no25-december-2022/machine-and-logistic-regression-algorithm

Keywords

Novel support vector machine, Machine Learning, Logistic Regression, Coronary disease, Accuracy, Prediction, Samples

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