Advanced Machine Learning Techniques for Early Prediction of Heart Disease: A Comprehensive Analysis

The major causes of death worldwide are heart diseases, and they call for highly sophisticated prediction models for early detection and treatment. This research paper compares the output of ML algorithms, Logistic Regression with an accuracy of 82.8%, Decision Tree Classifier with 70.3% accuracy, and Random Forest Classifier with 78.1 % accuracy, in predicting heart disease. Every model is evaluated in line with accuracy, sensitivity, specificity, and the area under the ROC curve (AUC-ROC). The findings demonstrate clear differences in model performance; however, this study emphasizes the prospects of ML techniques in enhancing heart disease prediction, thereby providing researchers and medical practitioners with interesting analysis. By means of the discovery of the most effective ML methods, our work helps the ongoing endeavors to improve diagnosis accuracy and patient outcomes in cardiovascular illnesses.