Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Support Vector Machine

Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Support Vector Machine. Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Support Vector Machine. Each group consists of a sample size of 10 and the study parameters inclu...

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Bibliographic Details
Published inCardiometry no. 25; pp. 1711 - 1717
Main Authors Karthi, C B M, Kalaivani, A
Format Journal Article
LanguageEnglish
Published Moscow Russian New University 01.12.2022
Subjects
Accuracy
Age
Blood pressure
Cardiovascular disease
Data analysis
Datasets
Forecasting techniques
Heart
Information technology
Regression analysis
Statistical significance
Support vector machines
Variables
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Summary:Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Support Vector Machine. Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Support Vector Machine. Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value of 0.8. Results: The Novel Logistic Regression (91.60) achieved improved accuracy than the Support Vector Machine (91.83) in Heart Disease Prediction. The statistical significance difference (two-tailed) is 0.01 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than the Support Vector Machine in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction.
ISSN:2304-7232
DOI:10.18137/cardiometry.2022.25.17111717