{"id":"W4416772021","doi":"10.3389/fcdhc.2025.1697769","title":"Voice-based prediction of prediabetes using classical machine learning models","year":2025,"lang":"en","type":"article","venue":"Frontiers in Clinical Diabetes and Healthcare","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber Polytechnic; General Electric (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prediabetes; Predictive modelling; Training set; Data modeling; Real world data","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001897999,0.0002327404,0.000774449,0.000408983,0.0002042466,0.0000570132,0.0005233518,0.0003998847,0.000001781077],"category_scores_gemma":[0.0008083033,0.000227852,0.0001496522,0.000731664,0.0002119502,0.000279815,0.0002722629,0.001338581,4.555757e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196611,"about_ca_system_score_gemma":0.0004074073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001993817,"about_ca_topic_score_gemma":0.00004130893,"domain_scores_codex":[0.9960154,0.001003412,0.001327254,0.0007581057,0.0003155292,0.000580286],"domain_scores_gemma":[0.9977742,0.0009489221,0.000339426,0.0005067656,0.0001933544,0.0002372913],"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":[0.00002026652,0.000077761,0.9236369,0.0006664874,0.00002141319,0.000001393869,0.0001059848,0.006389573,0.000004820259,0.002835059,0.0002365911,0.06600371],"study_design_scores_gemma":[0.0007049915,0.0003237902,0.1622289,0.0005218686,0.00001301282,1.267066e-7,0.00003639298,0.8261806,0.00001627905,0.009236553,0.0006182329,0.0001191899],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6743024,0.01356761,0.2939658,0.01344414,0.00330064,0.0008434884,0.00007442319,0.000306991,0.0001945221],"genre_scores_gemma":[0.9108475,0.0003331677,0.08757994,0.001039096,0.0000936586,0.00002296686,0.00002220123,0.00001719776,0.00004428909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8197911,"threshold_uncertainty_score":0.9291537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06668392626749577,"score_gpt":0.3480473825105082,"score_spread":0.2813634562430124,"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."}}