{"id":"W4286517661","doi":"10.18280/ria.360301","title":"Diagnostic Analysis of Diabetes Mellitus Using Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Artificial intelligence; Decision tree; Naive Bayes classifier; Random forest; Logistic regression; Diabetes mellitus; Boosting (machine learning); Computer science; Feature selection; Gradient boosting; Statistical classification; Medicine; Algorithm; Support vector machine","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002531021,0.0003048383,0.000891714,0.0008996942,0.002461542,0.00001409771,0.0007158236,0.0001622769,0.005354966],"category_scores_gemma":[0.001891285,0.0003276789,0.0003943978,0.004274015,0.0002085238,0.0001311219,0.0006156093,0.001648555,0.0001583642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004463274,"about_ca_system_score_gemma":0.0002364472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003101626,"about_ca_topic_score_gemma":0.0003330544,"domain_scores_codex":[0.9940348,0.001755208,0.001860808,0.0007408354,0.0005839046,0.00102448],"domain_scores_gemma":[0.9936169,0.004093641,0.0008645293,0.0008383272,0.0003677683,0.0002188833],"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":[0.00002162699,0.0002038024,0.3106341,0.0002576358,0.0002235836,0.000004906186,0.007832399,0.6741026,0.001361434,0.002465136,0.00003511014,0.002857676],"study_design_scores_gemma":[0.00002573641,0.0001591392,0.0006037598,0.00007380041,0.0004719322,0.000001374246,0.02075291,0.9700164,0.003505053,0.0005582258,0.003526158,0.0003055168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628702,0.003581858,0.02845883,0.0002641774,0.000859577,0.001231901,0.0001543358,0.0001727638,0.002406352],"genre_scores_gemma":[0.9966486,0.0001791375,0.001248776,0.0002161409,0.0001402009,0.0003313723,0.0001681224,0.00006104291,0.001006642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3100303,"threshold_uncertainty_score":0.9999175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530480614338861,"score_gpt":0.4136128981573913,"score_spread":0.2605648367235052,"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."}}