{"id":"W4412048689","doi":"10.1016/j.ijcard.2025.133584","title":"Predicting coronary artery disease using machine learning with optimized feature selection","year":2025,"lang":"en","type":"letter","venue":"International Journal of Cardiology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill Genome Centre","funders":"Lady Davis Institute for Medical Research; Canadian Institutes of Health Research; Watanabe Foundation; TD Bank; Japan Student Services Organization; Fonds de Recherche du Québec - Santé","keywords":"Medicine; Coronary artery disease; Feature selection; Cardiology; Selection (genetic algorithm); Feature (linguistics); Internal medicine; Artificial intelligence; Disease; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001351387,0.0002567033,0.0009181529,0.0005343783,0.0002989601,0.00100932,0.0005884744,0.002741599,0.001512612],"category_scores_gemma":[0.009188293,0.000201659,0.0005986674,0.0003771191,0.000398118,0.0005822187,0.000236886,0.003716143,0.001134736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000369897,"about_ca_system_score_gemma":0.0004851126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478833,"about_ca_topic_score_gemma":0.004497598,"domain_scores_codex":[0.9994467,0.0002204665,0.0000714403,0.00005438987,0.000146073,0.00006087358],"domain_scores_gemma":[0.9952391,0.003623756,0.0001452192,0.0001563586,0.0006873832,0.0001481042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002720793,0.0009301482,0.0730379,0.0001614717,0.0003158173,0.006964924,0.0002174878,0.008256063,0.009010248,0.00505852,0.2922446,0.6010821],"study_design_scores_gemma":[0.002189824,0.00295138,0.1012047,0.0005092301,0.000729131,0.03027434,0.0008684455,0.5471864,0.01433271,0.07860652,0.2207931,0.0003543109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1841375,0.005451933,0.06823816,0.7059112,0.01494997,0.0002150089,0.001123727,0.0007287287,0.01924372],"genre_scores_gemma":[0.7595128,0.003881363,0.0720743,0.1135852,0.03320057,0.0002391494,0.001040346,0.000123906,0.01634237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002741599,"threshold_uncertainty_score":0.007146895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06951126247498594,"score_gpt":0.4230502619147882,"score_spread":0.3535389994398023,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}