Upadhyay Dishita N.
Artificial Intelligence subdomain Machine learning
May 2025
Coronary artery disease (CAD) is one of the main causes of mortality worldwide, and early detection is crucial to ensuring proper treatment and preventing major outcomes. This work focuses on using machine learning (ML) algorithms to predict CAD based on risk factors and patient symptoms. A few machine learning models, such as Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting were evaluated and ranked using important assessment criteria including accuracy, precision, recall and AUC-ROC. The results indicate that early diagnosis may be significantly improved by machine learning (ML)-based models demonstrated the greatest accuracy of 92%. These findings show that ML models have the potential to become inexpensive, non-invasive CAD screening tools that might someday take the place of traditional diagnostic methods.
80- 92
© Airo Journals 2021
