An enhanced novel prediction of occurrence of cardiovascular disease by comparing decision tree over logistic regression.

Predicting the onset of cardiovascular disease is the goal. Images are separated into labelled and unlabeled categories using two machine learning algorithms: Decision Tree and Logistic regression. In order to efficiently and accurately analyse labelled pictures with G power in 80% and threshold 0.05 percent, CI 95% mean and standard deviation, the sample size was iterated 10 times from n =5 in Decision Tree and n = 5 in Logistic Regression. Accurate predictions and classifications of values from cardiac patient data have been generated in this study by comparing the predictive and classifying abilities of Decision Tree and Logistic Regression. The accuracy of Decision Tree (72.40%) is statistically significant (p0.05) greater than that of Logistic Regression (67.59%). Decision Tree outperforms Logistic Regression in predicting cardiovascular disease. [ABSTRACT FROM AUTHOR]