{"id":"W3185516026","doi":"10.1016/j.compbiomed.2021.104672","title":"Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":482,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; University of Saskatchewan","keywords":"Machine learning; Random forest; Artificial intelligence; Decision tree; Computer science; Feature (linguistics); Heart disease; k-nearest neighbors algorithm; Statistical classification; Algorithm; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005982282,0.0001159195,0.000427687,0.0002265249,0.0005180545,0.000002472751,0.00004947055,0.0001457731,0.00007527498],"category_scores_gemma":[0.0002349792,0.00009719242,0.00002056305,0.0004678734,0.0002425957,0.00005143218,0.0001093663,0.0005910379,0.000002330409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005962461,"about_ca_system_score_gemma":0.00008950282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009137397,"about_ca_topic_score_gemma":0.0004373064,"domain_scores_codex":[0.9981442,0.0006635993,0.0004962327,0.0003346635,0.00006985245,0.0002914192],"domain_scores_gemma":[0.9989153,0.0005680665,0.00008817927,0.0001407082,0.000105151,0.0001826607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005319698,0.00001709594,0.9878607,0.0001321536,0.00003167629,0.00000706628,0.002113581,0.0003619106,0.000184814,0.00009716245,0.00004273017,0.009097891],"study_design_scores_gemma":[0.0002483897,0.0001007623,0.3955416,0.0002465267,0.0000780576,0.000002922774,0.001186629,0.601667,0.00001386369,0.0001897613,0.0006650875,0.00005942615],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775842,0.004786861,0.01350392,0.003108873,0.0007308576,0.0002019223,0.000009200643,0.00004055287,0.00003354469],"genre_scores_gemma":[0.9948733,0.0009378816,0.002361786,0.001336972,0.0002953357,0.00001122731,0.0001464032,0.000006928236,0.00003011583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6013051,"threshold_uncertainty_score":0.3984509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1342426695278869,"score_gpt":0.478304892603834,"score_spread":0.344062223075947,"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."}}