{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003054986,0.001076572,0.0007515895,0.001228382,0.0003245847,0.001106705,0.0009942368,0.0008276532,0.002351362],"category_scores_gemma":[0.005640927,0.0002536647,0.001157226,0.0006658937,0.0003168435,0.0004975842,0.0006985828,0.001356928,0.00101159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481024,"about_ca_system_score_gemma":0.000899233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01591224,"about_ca_topic_score_gemma":0.007984372,"domain_scores_codex":[0.9993717,0.0002317021,0.00003925459,0.000195604,0.00007446144,0.00008741122],"domain_scores_gemma":[0.9971141,0.002050258,0.0001404232,0.0001378281,0.0004513632,0.000106148],"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":[0.001489133,0.0006004972,0.07508321,0.0002024713,0.0004775297,0.0002933226,0.0002047812,0.7164935,0.001743767,0.001035574,0.006112184,0.1962641],"study_design_scores_gemma":[0.00001266229,0.00005913708,0.005778227,0.000023552,0.00003330962,0.00002730485,0.00002300194,0.9927174,0.0002766316,0.0007368708,0.0002999651,0.00001189754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8057302,0.004454368,0.1779727,0.001378301,0.000333809,0.0001917235,0.003432078,0.002046462,0.004460536],"genre_scores_gemma":[0.9706463,0.0004384553,0.02343024,0.0001667263,0.0001021523,0.00008916634,0.003070359,0.0000519707,0.002004565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01591224,"threshold_uncertainty_score":0.03163922,"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."}}