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Original research

Classification and prediction of heart disease using novel random forest algorithm by comparing logistic regression for obtaining better accuracy

Abstract

Aim: Heart attacks are usually caused due to blockages, partially or completely, of the heart’s veins or arteries that constrict the flow of blood from or to the heart. The primary objective of this review aims to be seen as the most appropriate algorithm to give us the ideal prediction. We will be comparing the novel Random forest with Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 143 samples with novel Random Forest and Logistic Regression is executed with varying training and testing splits for foreseeing the accuracy of coronary disease prediction with the 80% of G-power value and heart disease data were gathered from multiple web sources, including latest The study's findings and criterion were 0.05%, with a 95% probability value, average, and confidence interval. The performance accuracy rate of the classifiers is used to evaluate the coronary disease dataset. There was a statistically significant value test between the novel Random Forest and Logistic Regression is 0.046 (p<0.05). Results and Discussion: The accuracy of predicting coronary disease in the novel Random Forest 90.16 % and Logistic Regression 85.25 % is obtained. Conclusion: This study concludes that the Prediction of Coronary disease using the novel Random Forest (RF) algorithm looks to be fundamentally superior to the Logistic Regression (LR) with increased precision.

Imprint

T Poojitha, Mahaveerakannan R. Classification and Prediction of Heart Disease using Novel Random Forest Algorithm by Comparing Logistic Regression for Obtaining Better Accuracy. Cardiometry; Issue 25; December 2022; p.1538-1545; DOI: 10.18137/cardiometry.2022.25.15381545; Available from: https://www.cardiometry.net/issues/no25-december-2022/classification-prediction-heart-disease

Keywords

Coronary Disease,   Novel Random Forest Algorithm,   Logistic Regression,   Machine Learning,   Classification,   Prediction
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