{"id":"W2963565281","doi":"10.1016/j.cmpb.2019.104992","title":"A new machine learning technique for an accurate diagnosis of coronary artery disease","year":2019,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":340,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Machine learning; CAD; Artificial intelligence; Normalization (sociology); Particle swarm optimization; Preprocessor; Classifier (UML); Support vector machine; Coronary artery disease; Feature selection; Data pre-processing; Data mining; 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":[0.0007521288,0.0004579334,0.0005327239,0.00124342,0.000400237,0.0006900307,0.0006492776,0.0008898354,0.002924666],"category_scores_gemma":[0.002085202,0.0002375143,0.0005314972,0.0009355047,0.0003428493,0.001184753,0.000638704,0.0009747396,0.001569858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002101901,"about_ca_system_score_gemma":0.0004511902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008006758,"about_ca_topic_score_gemma":0.00139227,"domain_scores_codex":[0.9995112,0.00007489434,0.00003664785,0.0000979082,0.0002510287,0.000028219],"domain_scores_gemma":[0.9990849,0.0004421903,0.00005491642,0.00009287916,0.000292399,0.00003269554],"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":[0.0001741932,0.0001339899,0.00204787,0.0001961312,0.0001064183,0.0002083589,0.00006767894,0.009518446,0.06008061,0.007271552,0.007396886,0.912798],"study_design_scores_gemma":[0.00009017222,0.0004091945,0.006570844,0.00009156497,0.0002561443,0.00268026,0.00006271881,0.85822,0.06216664,0.01531047,0.05404929,0.00009270741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01046882,0.00162183,0.9833171,0.0004117346,0.0006398415,0.00006831318,0.0001457736,0.0008528036,0.002473778],"genre_scores_gemma":[0.1131716,0.001607337,0.8745164,0.0004735005,0.000721337,0.0001376798,0.000286963,0.00009166906,0.008993474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002924666,"threshold_uncertainty_score":0.009784043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.247954431720299,"score_gpt":0.53739828263951,"score_spread":0.289443850919211,"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."}}