{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005452795,0.0008011402,0.001539971,0.001973171,0.0004179723,0.0009243076,0.0009422321,0.001126961,0.001165919],"category_scores_gemma":[0.0121709,0.0001825028,0.0009497146,0.001151762,0.0003006255,0.001099196,0.0006470565,0.0007446702,0.0006494023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006067858,"about_ca_system_score_gemma":0.001135449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003305315,"about_ca_topic_score_gemma":0.002100909,"domain_scores_codex":[0.9975568,0.001049986,0.0002851841,0.0003646889,0.0006036657,0.0001396562],"domain_scores_gemma":[0.983223,0.01164312,0.0005538279,0.001148416,0.003102409,0.0003291643],"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":[0.005752687,0.002700271,0.1283836,0.0005511958,0.00176062,0.0001290115,0.0001355391,0.2477633,0.003548978,0.0011757,0.008705983,0.5993932],"study_design_scores_gemma":[0.0001312234,0.001193561,0.0208961,0.0000302756,0.00020603,0.000144393,0.00006706188,0.971951,0.003666896,0.001012084,0.0006740283,0.00002747096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073557,0.004860937,0.07860541,0.0005187896,0.0003834096,0.0001886041,0.002016279,0.002170134,0.003900607],"genre_scores_gemma":[0.958716,0.0007941965,0.03483501,0.00006945448,0.0001476883,0.0001063637,0.004074937,0.00008012128,0.001176111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005452795,"threshold_uncertainty_score":0.0288375,"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."}}