{"id":"W4385863356","doi":"10.2196/48290","title":"Title: Prediction of Diabetes Using Machine Learning and Data Mining Algorithm: Cross-sectional study (Preprint)","year":2023,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; Machine learning; Algorithm; Artificial intelligence; Data mining; World Wide Web","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":[0.001046576,0.0003188946,0.0004710158,0.0005091468,0.0004303606,0.0009447627,0.0003958441,0.000985541,0.01246362],"category_scores_gemma":[0.005573209,0.0003606836,0.0004871761,0.0006411929,0.0002068875,0.0006715395,0.0002632012,0.001189334,0.002424401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263134,"about_ca_system_score_gemma":0.000290575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151232,"about_ca_topic_score_gemma":0.0005688128,"domain_scores_codex":[0.9995494,0.0001006225,0.00007575825,0.0001494432,0.00007911286,0.00004554301],"domain_scores_gemma":[0.9957924,0.001915433,0.0006131561,0.0003489825,0.0008766445,0.0004534003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00209117,0.0009529029,0.9882224,0.00008008601,0.0002618767,0.000203356,0.0002819697,0.00009860551,0.0007696315,0.00006099501,0.00263232,0.004344566],"study_design_scores_gemma":[0.00007809135,0.001229259,0.9956902,0.00002274587,0.0001905212,0.0004896725,0.0003549618,0.0005753704,0.0004152318,0.0001017762,0.0008421044,0.00001008522],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964519,0.0002523352,0.0003292321,0.0002589489,0.0002000775,0.000021057,0.001829048,0.00001914211,0.0006383573],"genre_scores_gemma":[0.9919865,0.0002298258,0.0002870326,0.0001902431,0.0002640828,0.00004666164,0.002457355,0.00002148326,0.00451667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01246362,"threshold_uncertainty_score":0.041695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5610001828273812,"score_gpt":0.6600243471782699,"score_spread":0.09902416435088868,"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."}}