{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006714101,0.001281096,0.001548386,0.001359095,0.0004997179,0.0007530111,0.001200957,0.0009994197,0.0008512509],"category_scores_gemma":[0.00943925,0.000417,0.001545255,0.001023839,0.0002481641,0.000962251,0.000860587,0.001972873,0.0002775074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009053982,"about_ca_system_score_gemma":0.001218102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01603287,"about_ca_topic_score_gemma":0.009637684,"domain_scores_codex":[0.9990286,0.0004984622,0.00006954643,0.0001669284,0.0001503758,0.0000860923],"domain_scores_gemma":[0.9932121,0.004724299,0.0003594139,0.0004230087,0.001153816,0.0001273098],"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":[0.0002017179,0.0002108315,0.01940529,0.00006371283,0.0002922412,0.00006059633,0.00004942442,0.9169026,0.0003945548,0.0005426618,0.001254081,0.06062225],"study_design_scores_gemma":[0.000007917916,0.00004153751,0.001189592,0.00001185898,0.00002704491,0.000008483601,0.000007939504,0.9980234,0.0001762594,0.0003999861,0.00009960862,0.000006492895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5951173,0.003234874,0.395438,0.0008621278,0.0001958869,0.0003533276,0.001569523,0.001054489,0.002174391],"genre_scores_gemma":[0.9113525,0.0008186362,0.0849426,0.000117368,0.0000654182,0.0003687484,0.001537563,0.00003947599,0.0007576174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01603287,"threshold_uncertainty_score":0.03550804,"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."}}