{"id":"W3010096987","doi":"10.2196/17110","title":"Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Council; National Science Foundation","keywords":"Metabolic syndrome; Machine learning; Logistic regression; Random forest; Decision tree; Artificial intelligence; Computer science; Multivariate statistics; Glycated hemoglobin; Receiver operating characteristic; Decision tree learning; Medicine; Algorithm; Internal medicine; Diabetes mellitus; Obesity","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":[0.003875553,0.000475265,0.0004964238,0.001066734,0.0004327509,0.0007600953,0.0004773699,0.0005870425,0.00117478],"category_scores_gemma":[0.007124177,0.0004666563,0.0008934896,0.0007386848,0.0002379182,0.0006733654,0.000399808,0.000890931,0.0003101397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003425454,"about_ca_system_score_gemma":0.0004366527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003928718,"about_ca_topic_score_gemma":0.002497769,"domain_scores_codex":[0.9989077,0.000513637,0.00009940001,0.0002009453,0.0001801179,0.00009808806],"domain_scores_gemma":[0.9968136,0.001629478,0.0004171873,0.0005084875,0.0003943395,0.0002368643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007764637,0.0004640387,0.9941428,0.0000103654,0.0001192656,0.0001280174,0.00008972041,0.0007382907,0.0001478245,0.00003930034,0.0001445045,0.003199408],"study_design_scores_gemma":[0.0003238013,0.006048113,0.9327036,0.00004589139,0.0004898967,0.001162954,0.0007620638,0.05663227,0.0006614649,0.0003737597,0.0007415912,0.00005466305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982948,0.0000714882,0.001215056,0.00001989577,0.000005877024,0.00004354859,0.0002279101,0.000008572527,0.0001129712],"genre_scores_gemma":[0.9981112,0.00007660632,0.001175067,0.00001195316,0.000008904402,0.00004616475,0.0004283073,0.000005022607,0.0001367249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003928718,"threshold_uncertainty_score":0.02049613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1184872286679597,"score_gpt":0.4353079736208758,"score_spread":0.3168207449529161,"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."}}