{"id":"W2943089774","doi":"10.1109/ccoms.2019.8821633","title":"Performance Improvement of Decision Trees for Diagnosis of Coronary Artery Disease Using Multi Filtering Approach","year":2019,"lang":"en","type":"article","venue":"2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"C4.5 algorithm; Circumflex; Decision tree; Cardiology; Computer science; Coronary artery disease; Tree (set theory); Decision tree learning; Right coronary artery; Internal medicine; Medicine; Artery; Pattern recognition (psychology); Artificial intelligence; Mathematics; Myocardial infarction; Coronary angiography; Support vector machine","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":[0.004789788,0.001272933,0.001520953,0.003362302,0.0008072839,0.001249649,0.0009587763,0.001591782,0.001162944],"category_scores_gemma":[0.008096119,0.0002768644,0.001794185,0.001620746,0.0002043628,0.0012012,0.0006233354,0.001054065,0.0006239601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008897939,"about_ca_system_score_gemma":0.001312595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413205,"about_ca_topic_score_gemma":0.008662835,"domain_scores_codex":[0.9979503,0.0006954583,0.0002757148,0.0004154665,0.0004118855,0.000251238],"domain_scores_gemma":[0.9958753,0.002626926,0.0001943487,0.0001785319,0.000901057,0.0002238762],"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":[0.002273872,0.0007922505,0.04079457,0.0003644987,0.0007113916,0.0003502435,0.0002822443,0.281592,0.008136106,0.001416395,0.009356562,0.6539298],"study_design_scores_gemma":[0.00003856179,0.0002678286,0.005903426,0.00004523007,0.0001211474,0.0001091942,0.00008606067,0.9889044,0.002227396,0.001262394,0.001013209,0.00002117447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6040727,0.01013151,0.3715337,0.001753739,0.0008512304,0.000219989,0.002958041,0.002985154,0.005493769],"genre_scores_gemma":[0.895532,0.0009838819,0.09761299,0.0002777409,0.0001993357,0.00007061656,0.004021083,0.00005517648,0.001247234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01413205,"threshold_uncertainty_score":0.0280996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2532735119755011,"score_gpt":0.4370091264381438,"score_spread":0.1837356144626427,"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."}}