{"id":"W2958091182","doi":"10.18280/rces.050301","title":"Classification of heart disease using multiple classifiers","year":2019,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005008514,0.0001362784,0.0006182438,0.00008247744,0.00007420075,0.000001041001,0.0001321446,0.00004880887,0.00002224853],"category_scores_gemma":[0.0004200708,0.0001185346,0.0001085949,0.0002291779,0.0000415685,0.00007090286,0.0001091689,0.0002028954,0.00004332538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122564,"about_ca_system_score_gemma":0.000113218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003004754,"about_ca_topic_score_gemma":0.000002687754,"domain_scores_codex":[0.9983336,0.0001470995,0.0008850022,0.0001897437,0.0002064193,0.0002381024],"domain_scores_gemma":[0.9980009,0.0007871112,0.0003037684,0.0003698274,0.000465292,0.00007309595],"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":[0.00003673454,0.00008598143,0.6517759,0.3139444,0.0002201662,0.000001693768,0.002188739,0.01367145,0.002280487,0.004985201,0.004362504,0.006446699],"study_design_scores_gemma":[0.0002567267,0.0001415138,0.1547305,0.154665,0.0001587674,0.000001116048,0.001061384,0.6609187,0.0001809283,0.0001393771,0.02727532,0.0004706959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7395001,0.2275728,0.02351546,0.002352455,0.003691019,0.003118081,0.00002899459,0.0001555777,0.0000654863],"genre_scores_gemma":[0.9758475,0.01444379,0.00897259,0.0004326989,0.0002014918,0.00004813648,0.000005065131,0.0000251034,0.0000235508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6472472,"threshold_uncertainty_score":0.48337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2172897342864206,"score_gpt":0.4831938412163425,"score_spread":0.2659041069299218,"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."}}