Why the study?
Heart disease remains a leading cause of mortality worldwide, necessitating early detection and prevention strategies.
Do machine learning and deep learning algorithms improve the prediction accuracy of heart disease compared to traditional models?
Do machine learning and deep learning algorithms improve the prediction accuracy of heart disease compared to traditional models?
Machine learning models, particularly Random Forest, demonstrate high accuracy (95%) in predicting heart disease, highlighting the potential of AI-driven diagnostic tools to enhance early detection in clinical settings.
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ML models may enhance heart disease prediction; leaves open prospective validation against traditional scores before clinical use.
Heart disease remains a leading cause of mortality worldwide, necessitating early detection and prevention strategies. This study explores machine learning (ML) approaches for predicting heart disease using patient datasets. Various ML algorithms, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, XGBoost, and an Artificial Neural Network (ANN), were implemented to classify heart disease presence. The Random Forest model achieved the highest accuracy of 95%. The findings demonstrate that ML can significantly enhance heart disease prediction, aiding early diagnosis and treatment.
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Vijayasimha et al. (2025) studied this question.
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