Why the study?
Prior machine learning studies relying on multimodal clinical, imaging, and genetic data do not provide early screening or prognosis, leaving the feasibility of using lifestyle and medical history variables alone unclear.
Can machine learning models using lifestyle and medical history data accurately predict CAD prognosis compared to clinicians?
Population
571 participants with and without CAD
Comparison
Multiple ML models using lifestyle and medical history variables vs clinician baseline
Design
Cohort study with 10-fold cross-validation
Key result
Machine learning models using lifestyle and medical history variables achieved up to 76% accuracy for CAD prognosis, compared to a clinician baseline of 78.8%.
Authors
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ML models approach clinician accuracy for CAD prognosis; leaves open their role as adjunct screening tools pending validation.
Observational (n=571)
Can machine learning models using lifestyle and medical history data accurately predict CAD prognosis compared to clinicians?
Absolute Event Rate: 76% vs 78.8%
Machine learning models using only lifestyle and medical history data achieve moderate accuracy (up to 76%) for CAD prognosis, slightly lower than clinician baseline, highlighting their potential as complementary early screening tools.
Samaras et al. (2026) conducted an observational in Coronary artery disease (CAD) (n=571). Machine learning models using lifestyle and medical history variables vs. Clinician baseline was evaluated on Predictive accuracy for CAD prognosis. Machine learning models using lifestyle and medical history variables achieved up to 76% accuracy for CAD prognosis, compared to a clinician baseline of 78.8%.