Authors : Latthika S

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/5n83r4j2

Scribd : https://tinyurl.com/4yxey4m8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP382

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Heart diseases including heart attacks, cause about 31% of global deaths, remaining a significant health threat despite preventability. Limited tech advancements and awareness, especially in developing nations, amplify this challenge. Machine learning offers promise in tackling this issue, with studies advocating ensemble methods for accurate predictive models. These models analyze extensive medical data to efficiently predict heart diseases, undergoing stages like data exploration, feature selection, model implementation, and comparative analysis. A model using Logistic Regression, Naive Bayes, and Random Forest initially identified top-performing models, later refined to CatBoost, RandomForest, and XGBoost through cross-validation and tuning. A hybrid model, combining Logistic Regression, CatBoost, and RandomForest, achieved a 97% accuracy, showcasing improved precision, recall, F1 score, and ROC AUC. This underscores machine learning's potential in enhancing predictive accuracy and refining strategies to combat heart diseases effectively.

Keywords : Logistic Regression(LR), K-Nearest Neighbors(KNN), RandomForest(RF), CatBoost(CB), XSBoost (XSB), Stochastic Gradient Descent(SGD), Cross- Validation(CV), Support Vector Machine(SVM) Hyperparameter Tuning(HT) and Voting Classifier(VC).

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