{"id":"W2979103454","doi":"10.1109/iccchina.2019.8855897","title":"A High Accuracy Integrated Bagging-Fuzzy-GBDT Prediction Algorithm for Heart Disease Diagnosis","year":2019,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Boosting (machine learning); Fuzzy logic; Machine learning; Artificial intelligence; Heart disease; Computational intelligence; Data mining; Decision tree; Algorithm; Precision and recall; Medicine; Internal 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":[0.001251589,0.0007272831,0.001197102,0.001110648,0.0008234999,0.0008679516,0.001444261,0.001080972,0.0009379221],"category_scores_gemma":[0.003062275,0.0003398299,0.0009120521,0.0009665359,0.000300145,0.001099989,0.0008480412,0.0009458101,0.0003309623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008447865,"about_ca_system_score_gemma":0.001300866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420865,"about_ca_topic_score_gemma":0.01100409,"domain_scores_codex":[0.9992779,0.00009984717,0.00007477367,0.00016692,0.0002917905,0.00008880304],"domain_scores_gemma":[0.9991236,0.0003282398,0.00006310569,0.00005059758,0.0003859033,0.00004853224],"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":[0.000288278,0.000219709,0.007546695,0.0000942737,0.0001249139,0.0001776428,0.0001441674,0.4463854,0.006887418,0.003499938,0.003632375,0.5309992],"study_design_scores_gemma":[0.00001047868,0.00002862866,0.0004776389,0.000005700771,0.00001685946,0.00003591708,0.000007359209,0.9974381,0.0007332162,0.0009086301,0.0003299854,0.00000733626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04220469,0.0006251473,0.9551746,0.0002246724,0.0001152694,0.0000724968,0.00009185367,0.000549415,0.0009418516],"genre_scores_gemma":[0.6845919,0.0004660812,0.3113507,0.0002822201,0.0001144939,0.0001826482,0.0004253012,0.00004459539,0.002542013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01420865,"threshold_uncertainty_score":0.02825189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09117491431252392,"score_gpt":0.4437618975627365,"score_spread":0.3525869832502125,"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."}}