Accurate Prediction of Myocardial Infarction By Comparing Logistic Regression Algorithm with CatBoost Classifier

Aim: The forecast of Myocardial Infarction for humans employing a Machine learning model by corresponding Logistic Regression Algorithm with CatBoost Classifier. accuracy is enhanced utilizing the novel LR Materials and Methods: study utilized total 20 sample iterations, 10 samples per group. Group 1 was analyzed using logistic regression algorithm, while 2 decision tree classifier. statistical power set at 80%, confidence level 95%. Results: outcome 94.61% Classifier 79.516%, both groups are statistically significant as p = 0.015 (<0.05) value in independent T-test between CB Conclusion: This research concludes that algorithm gives most accurate mortality difference 15.1%, compared to