Key result
Gradient Boosting Machines (GBM) achieved the highest performance in predicting heart disease with an accuracy of 88% and an ROC-AUC of 0.91, outperforming Artificial Neural Networks and K-Nearest Neighbors.
Why the study?
Heart disease remains a major health concern, highlighting the importance of early detection and prevention of life-threatening heart-related conditions such as heart attacks and strokes.
Absolute Event Rate: 88% vs 85%
This review highlights the application and evaluation of various machine learning algorithms for the early detection and prediction of cardiovascular diseases.
ML algorithms merit evaluation for CVD prediction; leaves open prospective validation before clinical adoption.
Heart disease has been one of the major health concerns over the decades regardless of age, weight, or gender. Here in the article, we try to highlight the importance of early detection and the prevention of life-threatening heart-related diseases, including heart attacks and strokes. For this review paper, we include raw data from clinics and also include the famous Framingham Heart Study dataset, which is a popular cardiovascular dataset, including patient records that emphasize important human risk factors including age, blood pressure, cholesterol, smoking status, and family history. Precision, recall, accuracy, F1-score, and ROC-AUC metrics are such techniques and algorithms that are used to evaluate the implementation and performance of advanced machine learning approaches, including Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN) and many more. Here, we
No takes yet. Share an insight, caveat, or question.
Shaima et al. (2025) studied Cardiovascular disease. Gradient Boosting Machines (GBM) vs. Artificial Neural Networks (ANN) and K-Nearest Neighbors (KNN) was evaluated on Model accuracy for heart disease prediction. Gradient Boosting Machines (GBM) achieved the highest performance in predicting heart disease with an accuracy of 88% and an ROC-AUC of 0.91, outperforming Artificial Neural Networks and K-Nearest Neighbors.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: