{"id":"W4412798192","doi":"10.2196/70621","title":"Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study","year":2025,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prediabetes; Feature selection; Computer science; Selection (genetic algorithm); Artificial intelligence; Machine learning; Feature (linguistics); Pattern recognition (psychology); Medicine; Diabetes mellitus","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":[0.006437483,0.0008836195,0.00113457,0.001626523,0.0002086528,0.0006862552,0.0004441807,0.0005766933,0.0006661142],"category_scores_gemma":[0.009805528,0.0001506011,0.001142254,0.00139264,0.0002587045,0.0005743609,0.0003713327,0.0004781172,0.0001605238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005209871,"about_ca_system_score_gemma":0.0005691886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479947,"about_ca_topic_score_gemma":0.001079002,"domain_scores_codex":[0.9978816,0.001391225,0.0001182473,0.0002472052,0.0002450782,0.0001166399],"domain_scores_gemma":[0.993016,0.005694823,0.0002951716,0.0002497999,0.0006504254,0.00009371481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005746925,0.001547933,0.1287643,0.0007328643,0.001582723,0.000244186,0.0001358279,0.2269511,0.002624344,0.00110508,0.003712828,0.6268519],"study_design_scores_gemma":[0.0002452488,0.003219639,0.07521966,0.000112976,0.0006403335,0.0003221144,0.000130381,0.9132428,0.003415335,0.001403772,0.002001763,0.0000459445],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120758,0.01531085,0.06974792,0.0004332846,0.000111691,0.0001465063,0.0003883092,0.000333118,0.001452613],"genre_scores_gemma":[0.9677292,0.001847265,0.02911399,0.00007827929,0.0000713677,0.00009285946,0.000623384,0.0000385189,0.0004051278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006437483,"threshold_uncertainty_score":0.03404504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06289600529600652,"score_gpt":0.4114418252403894,"score_spread":0.3485458199443829,"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."}}