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Published on: August 9, 2024
Machine learning-based coronary artery disease diagnosis: A comprehensive review
Roohallah Alizadehsani1, Moloud Abdar2, Mohamad Roshanzamir3
1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.
Insights
Machine learning (ML) offers a noninvasive alternative to angiography for detecting coronary artery disease (CAD). However, variations in study methods prevent generalizing ML
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a leading cause of death and significant financial burden globally.
- Current diagnostic methods like angiography are invasive and carry risks.
- Machine learning (ML) presents a promising noninvasive, cost-effective approach for CAD detection.
Purpose of the Study:
- To conduct a comprehensive review of ML-based CAD diagnosis studies published between 1992 and 2019.
- To investigate the influence of dataset characteristics and ML techniques on diagnostic performance.
- To identify challenges and limitations in the current literature on ML for CAD.
Main Methods:
- Systematic literature review of studies on ML for CAD diagnosis.
- Analysis of factors including dataset size, geographical origin, features used, and stenosis data.
- Evaluation of various ML techniques, feature selection methods, and performance metrics.
Main Results:
- Significant heterogeneity exists across ML-based CAD studies regarding datasets and methodologies.
- Performance of ML models varies widely due to differences in data characteristics and applied techniques.
- Lack of standardization hinders the generalizability of reported ML achievements in CAD detection.
Conclusions:
- The variability in ML study designs impedes the reliable generalization of findings for CAD diagnosis.
- Further research is needed to standardize methodologies and validate ML models for clinical application.
- Addressing identified challenges is crucial for advancing ML-based CAD detection.
Abstract:
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
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