{"id":"W2025647920","doi":"10.5539/gjhs.v7n5p304","title":"Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining","year":2015,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Tabriz; Tabriz University of Medical Sciences; Iran University of Medical Sciences","keywords":"C4.5 algorithm; Decision tree; Medicine; Body mass index; Diabetes mellitus; Data mining; Data pre-processing; Decision tree learning; Type 2 diabetes; Decision tree model; Identification (biology); Computer science; Machine learning; Internal medicine; Support vector machine; Naive Bayes classifier; Endocrinology","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":[0.005717538,0.000890545,0.0008327243,0.002149517,0.0003945606,0.001257246,0.0008360544,0.0005567669,0.001066995],"category_scores_gemma":[0.01371082,0.0001890098,0.001496971,0.001785157,0.000170329,0.001093549,0.0004348168,0.0006680969,0.0002275333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005910802,"about_ca_system_score_gemma":0.001072642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005497676,"about_ca_topic_score_gemma":0.002567792,"domain_scores_codex":[0.9978232,0.001214394,0.0002045281,0.0002291501,0.0004257924,0.00010288],"domain_scores_gemma":[0.9878995,0.0103582,0.0003439525,0.000289201,0.0009545399,0.0001546342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001513533,0.001390366,0.4746876,0.0007670865,0.001279058,0.0008983498,0.0005720007,0.2555221,0.001251702,0.002744796,0.004589602,0.2547837],"study_design_scores_gemma":[0.00008534561,0.0004403083,0.0404682,0.0001598318,0.0004779446,0.0003554572,0.0003169082,0.951072,0.001399885,0.004019161,0.001175763,0.00002929536],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8794726,0.002133896,0.1095866,0.001601499,0.0001236818,0.0003108708,0.002576401,0.0004111474,0.003783291],"genre_scores_gemma":[0.9572858,0.0005426424,0.04031642,0.00006472316,0.00003795347,0.00008778031,0.001279299,0.00001749626,0.000367911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005717538,"threshold_uncertainty_score":0.03023756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4462131924051551,"score_gpt":0.548326548722445,"score_spread":0.10211335631729,"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."}}