The design of this system shows the efficient prediction of heart diseases by the implementation of machine learning multiple classifiers algorithm. The UCI Heart Disease Dataset is used, while the data preprocessing involves filling missing values, encoding of categorical values, scaling of features, and selection of important features. Three machine learning classifiers, namely Random Forest Classifier, Gradient Boosting Classifier, and Logistic Regression are implemented. The implementation involves training the machine learning models using the RandomizedSearchCV technique and cross-validation technique. The evaluation of the model's performance is done using measures such as ACCURACY, PRECISION, RECALL, and F1-SCORE. A

fair comparison is conducted to select the best performing model automatically using accuracy. Results show that the Random Forest model performed the best with an accuracy of 81.67%, compared to the rest of the models. Evaluation of feature importance shows key clinical features that significantly contribute to the accuracy.

Keywords— Heart Disease Prediction, Machine Learning, Random Forest, Gradient Boosting, Logistic Regression, Feature Selection, Classification, Healthcare Analytics, UCI Dataset, Predictive Modeling