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- W4367336095 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." @default.
- W4367336095 created "2023-04-30" @default.
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- W4367336095 date "2023-02-14" @default.
- W4367336095 modified "2023-09-25" @default.
- W4367336095 title "Analysis and Comparison of Prediction of Heart Disease Using Novel Support Vector Machine and Logistic Regression Algorithm" @default.
- W4367336095 doi "https://doi.org/10.18137/cardiometry.2022.25.783787" @default.
- W4367336095 hasPublicationYear "2023" @default.
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