{"id":"W2020502126","doi":"10.1016/j.diabres.2013.01.023","title":"Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach","year":2013,"lang":"en","type":"article","venue":"Diabetes Research and Clinical Practice","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Receiver operating characteristic; Medicine; Logistic regression; Artificial neural network; Multivariate statistics; Statistics; Area under the curve; Type 2 Diabetes Mellitus; Data set; Artificial intelligence; Machine learning; Diabetes mellitus; Internal medicine; Mathematics; Computer science; Endocrinology","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":[0.002474656,0.0008263037,0.00116361,0.002592969,0.0003910983,0.001451425,0.0007478063,0.001039473,0.001027473],"category_scores_gemma":[0.005221319,0.0001819017,0.0008485828,0.00106144,0.0002170565,0.0008451988,0.0004157771,0.0007176128,0.0001873128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005597064,"about_ca_system_score_gemma":0.0005386249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004516376,"about_ca_topic_score_gemma":0.003685368,"domain_scores_codex":[0.9989514,0.0003922127,0.0001518612,0.0001506472,0.0002767552,0.00007704345],"domain_scores_gemma":[0.9972377,0.00182392,0.0002328745,0.0001070881,0.0005024437,0.00009595518],"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":[0.00266971,0.001892673,0.4916573,0.0002103961,0.001249515,0.000386329,0.0001235173,0.1116143,0.005863959,0.002186736,0.002103858,0.3800415],"study_design_scores_gemma":[0.00005851828,0.0004441792,0.05904189,0.0000381117,0.0004243576,0.0001586421,0.000149907,0.9354122,0.001727301,0.002152367,0.000355139,0.00003733177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8450652,0.00164472,0.1471212,0.0008897649,0.0002075113,0.0001838903,0.0008855888,0.0002536984,0.003748391],"genre_scores_gemma":[0.97434,0.000318709,0.02398117,0.00005650092,0.00007477985,0.00005253888,0.0004367929,0.000006728665,0.0007328332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004516376,"threshold_uncertainty_score":0.01308733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6392326485622599,"score_gpt":0.6561973894622376,"score_spread":0.01696474089997768,"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."}}