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

Cardiometry

article

G. Pavithraa¹  Sivaprasad¹ 

[1] Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University

关键词: Novel support vector machine;    Machine Learning;    Logistic Regression;    Coronary disease;    Accuracy;    Prediction;    Samples;   

DOI  :  10.18137/cardiometry.2022.25.783787

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

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.

【 授权许可】

CC BY