Enhancing the Detection of Coronary Artery Disease Using Machine Learning
Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. Early detection is critical to recover patient outcomes and decrease healthcare costs. In recent years, machine learning (ML) advancements have shown significant potential in enhancing the accuracy of CAD diagnosis. This study investigates the application of ML algorithms to improve the detection of CAD by analyzing patient data, including clinical features, imaging, and biomarker profiles. Bi-directional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and a hybrid of Bi-LSTM+GRU were trained on large datasets to predict the presence of CAD. Results demonstrated that these ML models ...
| Authors | Karan Kumar Singh, Nikita Gajbhiye, Gouri Sankar Mishra |
|---|---|
| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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