{"id":"W4390550959","doi":"10.1109/rmkmate59243.2023.10369394","title":"Boruta Feature Selection for Prediction of Coronary Artery Disease","year":2023,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature selection; Computer science; Feature (linguistics); Selection (genetic algorithm); Artificial intelligence; Decision tree; Coronary artery disease; Rough set; Data mining; Set (abstract data type); Feature extraction; Pattern recognition (psychology); Machine learning; Cardiology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008628606,0.000453392,0.0008946212,0.001495815,0.0004239625,0.0006260695,0.0003606736,0.0004285787,0.00117707],"category_scores_gemma":[0.001829654,0.0001896832,0.0007827992,0.0009206657,0.0001507162,0.0003131734,0.000246226,0.0004237983,0.0004155451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576483,"about_ca_system_score_gemma":0.0006865701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004453351,"about_ca_topic_score_gemma":0.00289146,"domain_scores_codex":[0.9995773,0.0001373912,0.00003645561,0.00008495818,0.00008741195,0.00007636227],"domain_scores_gemma":[0.9994503,0.0002584243,0.00003498991,0.00003014116,0.0001948852,0.00003120635],"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":[0.00140764,0.0006430594,0.08742614,0.0002223751,0.0003325517,0.0004257487,0.0001815717,0.1366538,0.02261704,0.001350265,0.008703834,0.740036],"study_design_scores_gemma":[0.00008545167,0.0004120134,0.031631,0.00004266718,0.0001090125,0.0002942155,0.00007431509,0.953639,0.009007796,0.001511354,0.003156467,0.00003667192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6371021,0.002763194,0.3537679,0.0005587045,0.0003090056,0.0001814486,0.001009159,0.001748833,0.00255967],"genre_scores_gemma":[0.9360401,0.0003020946,0.06098131,0.00005879387,0.00005998854,0.0001076405,0.0008685612,0.00004246909,0.001539099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004453351,"threshold_uncertainty_score":0.008854866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1712584157690517,"score_gpt":0.474358137716327,"score_spread":0.3030997219472753,"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."}}