Key result
Top machine learning models achieve up to ~100% accuracy for heart disease prediction, outperforming KNN.
Why the study?
Reliable and efficient predictive models for early diagnosis and clinical decision support in heart disease are needed due to its status as a leading cause of mortality worldwide.
Can machine learning models accurately predict the presence of heart disease using structured clinical patient data?
Can machine learning models accurately predict the presence of heart disease using structured clinical patient data?
Absolute Event Rate: 1% vs 0.9348%
Machine learning models, particularly ensemble and boosting methods, demonstrated near-perfect accuracy in predicting heart disease from structured clinical data, highlighting their potential for clinical decision support pending external validation.
Requires external validation before clinical use; leaves open prospective trials to confirm ML utility.
Heart disease remains a leading cause of mortality worldwide, necessitating reliable and efficient predictive models for early diagnosis and clinical decision support. This study presents a comprehensive machine learning framework for heart disease prediction using a structured clinical dataset comprising 920 patient records with diverse demographic, physiological, and diagnostic attributes. The dataset includes both numerical and categorical features, requiring careful preprocessing and encoding to ensure compatibility across different model architectures.A range of classification algorithms is systematically evaluated, including Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance is assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve, along with five-fold cross-validation to examine stability and generalization behavior.The experimental results demonstrate consistently high predictive performance across most models, with several approaches achieving near-perfect classification metrics and minimal variation across cross-validation folds. In contrast, K-Nearest Neighbors exhibits slightly lower performance, highlighting differences in sensitivity to local data structure. Analysis of feature distributions and pairwise relationships indicates strong separability between classes, particularly driven by clinically relevant variables such as chest pain type, exercise-induced angina, ST depression, and maximum heart rate.Further evaluation using confusion matrices, receiver operating characteristic curves, and precision–recall curves confirms the robustness of the predictive models and their ability to distinguish between diseased and non-diseased cases with high reliability. Despite the strong performance, the study acknowledges potential dataset-specific characteristics that may influence model behavior and emphasizes the importance of external validation for clinical deployment.
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Rashid et al. (2024) studied Heart disease (n=920). Machine learning classification models (e.g., Logistic Regression, Random Forest, XGBoost) vs. K-Nearest Neighbors was evaluated on Classification accuracy. Machine learning models including Logistic Regression, Random Forest, and XGBoost achieved near-perfect classification metrics (accuracy up to 1.0000) for heart disease prediction, outperforming K-Nearest Neighbors (accuracy 0.9348).
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