{"id":"W2981121978","doi":"10.1186/s12902-019-0436-6","title":"Predictive models for diabetes mellitus using machine learning techniques","year":2019,"lang":"en","type":"article","venue":"BMC Endocrine Disorders","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Fields Institute for Research in Mathematical Sciences; University of Toronto; York University","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; Fields Institute for Research in Mathematical Sciences","keywords":"Logistic regression; Medicine; Decision tree; Diabetes mellitus; Random forest; Receiver operating characteristic; Machine learning; Artificial intelligence; Sensitivity (control systems); Body mass index; Predictive modelling; Decision tree model; Internal medicine; Statistics; Computer science; Mathematics; Endocrinology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002761102,0.001338009,0.0009897405,0.002994061,0.0004696444,0.001261448,0.001201122,0.0007750294,0.001866194],"category_scores_gemma":[0.0109119,0.0003513927,0.001105746,0.001750389,0.0003697759,0.0008482671,0.0006489544,0.001792437,0.0006894489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001265816,"about_ca_system_score_gemma":0.001551664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02642207,"about_ca_topic_score_gemma":0.01429421,"domain_scores_codex":[0.9992359,0.0003065919,0.00005542353,0.0001257043,0.0002037494,0.00007270758],"domain_scores_gemma":[0.9938186,0.005052581,0.000388132,0.000140335,0.0005318164,0.000068582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000121338,0.0001846626,0.02424848,0.0001849566,0.0002999669,0.0002403017,0.00008982759,0.8325652,0.0003773107,0.00422714,0.004141455,0.1333193],"study_design_scores_gemma":[0.000009336032,0.00001877433,0.001057551,0.00003445875,0.00003715621,0.00004300366,0.00001583563,0.9920303,0.0001821813,0.005883433,0.0006784007,0.000009554113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07006589,0.002452303,0.9172429,0.001786698,0.0001783466,0.0002631677,0.001469948,0.003356941,0.003183819],"genre_scores_gemma":[0.7889043,0.002075244,0.2035929,0.0004280618,0.0002809625,0.000423036,0.002147192,0.0001106358,0.002037647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02642207,"threshold_uncertainty_score":0.05253655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09883913857235904,"score_gpt":0.4360000462798314,"score_spread":0.3371609077074723,"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."}}