{"id":"W4388095729","doi":"10.5267/j.ijdns.2023.10.006","title":"Diagnosing diabetes mellitus using machine learning techniques","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Classifier (UML); Blindness; Machine learning; Feature selection; Computer science; Diabetes mellitus; Medicine; Seriousness; Pattern recognition (psychology); Optometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004743521,0.00008158203,0.0001676638,0.0002796171,0.0008679784,0.00006707435,0.001238879,0.0000546743,0.00004765204],"category_scores_gemma":[0.001366856,0.00006710159,0.00002173116,0.0006354104,0.0002718915,0.001137884,0.0009350848,0.0005800796,0.00001773183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113773,"about_ca_system_score_gemma":0.0003648193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000205041,"about_ca_topic_score_gemma":0.000102383,"domain_scores_codex":[0.9979088,0.0001818368,0.0006278216,0.0002160212,0.0006562498,0.0004093245],"domain_scores_gemma":[0.9974695,0.001039059,0.0004990657,0.0001927678,0.0006591458,0.0001404638],"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":[0.00002770997,0.00001394315,0.8848661,0.00002330931,0.00002261227,0.00004163009,0.0009610469,0.0007046808,0.00324012,0.0006042036,0.001187176,0.1083075],"study_design_scores_gemma":[0.0002638847,0.0002643239,0.0237305,0.003583151,0.00004637866,0.00006745362,0.003526713,0.8380822,0.00602822,0.01126232,0.1127166,0.0004283196],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911035,0.001474226,0.001875649,0.00229121,0.002864632,0.000143233,0.00003769332,0.00006235761,0.0001474796],"genre_scores_gemma":[0.9859092,0.003344992,0.008220979,0.0004899414,0.001970809,0.000002295155,0.00001931166,0.00001167832,0.00003075267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8611356,"threshold_uncertainty_score":0.6675876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2315234992809047,"score_gpt":0.5234032326575142,"score_spread":0.2918797333766096,"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."}}