Key result
Deep neural networks outperform other ML models, predicting heart disease during stress testing with 92% accuracy.
Why the study?
Predicting heart failure during physical exertion remains a major clinical challenge due to the dynamic nature of cardiac function under stress.
Do machine learning algorithms accurately predict heart disease during stress testing?
Do machine learning algorithms accurately predict heart disease during stress testing?
Deep neural networks and other machine learning algorithms demonstrate high accuracy (up to 92%) in predicting heart disease, highlighting their potential to support clinical diagnosis during stress testing.
May support DNNs for stress-test prediction; leaves open need for prospective validation.
Abstract; The heart is the most important organ in the human body. Given the dynamic nature of cardiac function under stress, predicting heart failure during physical exertion is a major challenge in clinical practice. Machine learning (ML) provides a transformative approach for analyzing complex physiological and clinical data and achieving accurate and timely predictions. This study aims to review the findings of previous studies on the use of AI in heart disease diagnosis. Several machine learning algorithms, including random forests, support vector machines (SVMs), deep neural networks (DNNs), and others, were evaluated to determine their predictive performance. The results showed that deep neural networks outperformed other models, achieving 92% accuracy and 94% sensitivity. Their ability to adapt to generally nonlinear patterns also demonstrate the importance of some features over others, such as cholesterol levels, blood pressure, chest pain, and heart rate. The study concludes by highlighting the importance of AI in supporting and assisting medical professionals, particularly in the field of heart health, which can advance beyond diagnosis and prediction.
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Azba et al. (2026) conducted a review in Heart disease and heart failure (n=1,000). Deep Neural Networks (DNN) vs. Other machine learning algorithms (e.g., SVM, Random Forest, Logistic Regression) was evaluated on Prediction accuracy. Deep neural networks outperformed other machine learning models in predicting heart disease during stress testing, achieving 92% accuracy and 94% sensitivity.
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