{"id":"W3005508070","doi":"10.2196/15431","title":"Ensemble Learning Models Based on Noninvasive Features for Type 2 Diabetes Screening: Model Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Science and Technology of Liaoning Province; China Association for Science and Technology","keywords":"Machine learning; Random forest; Artificial intelligence; Computer science; Ensemble learning; Linear discriminant analysis; Test set; Cross-validation; Population; Support vector machine; Predictive modelling; Ensemble forecasting; Set (abstract data type); Data mining; Medicine","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":[0.0006355756,0.000165689,0.0002640662,0.00007294163,0.0006758322,0.00001980211,0.0001635341,0.0003637398,0.00005696668],"category_scores_gemma":[0.001536626,0.0001400687,0.00003408589,0.0001629707,0.00005630076,0.0002161303,0.00009236062,0.0008943023,0.00006204432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007150385,"about_ca_system_score_gemma":0.0008194945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006369858,"about_ca_topic_score_gemma":0.00001307196,"domain_scores_codex":[0.9978824,0.0001086361,0.0008005697,0.0001426402,0.0006033165,0.0004624813],"domain_scores_gemma":[0.9976856,0.001122323,0.0002633075,0.0001212317,0.0003179534,0.0004896524],"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":[0.0007576784,0.0001426311,0.01055295,0.008188936,0.00008602569,0.000005891036,0.3218365,0.4325867,0.00006342983,0.01213047,0.03017802,0.1834708],"study_design_scores_gemma":[0.0002619467,0.0002118654,0.00003003011,0.0004368018,0.000007470236,1.619474e-7,0.007800919,0.9857924,0.001136843,0.0006077274,0.003563731,0.0001500805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5911171,0.00004996192,0.3940766,0.008323577,0.000242791,0.002634969,0.0000150782,0.0002899601,0.003249974],"genre_scores_gemma":[0.9361563,0.00001838016,0.04637867,0.01651951,0.0001983248,0.0004154449,0.0001544215,0.00003187621,0.0001270952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5532057,"threshold_uncertainty_score":0.5711838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2591769122550052,"score_gpt":0.450187147865009,"score_spread":0.1910102356100039,"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."}}