{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001160386,0.0005868577,0.0008601312,0.003922499,0.0003835027,0.001151085,0.0006653129,0.0006799793,0.001159134],"category_scores_gemma":[0.00351313,0.0001405985,0.000828946,0.001685735,0.0001644234,0.0006054934,0.0004318383,0.0006870826,0.0005544889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005153809,"about_ca_system_score_gemma":0.0008276764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559086,"about_ca_topic_score_gemma":0.001641696,"domain_scores_codex":[0.9990627,0.0002197792,0.0001335484,0.0001558676,0.0003158621,0.0001122832],"domain_scores_gemma":[0.9988341,0.0005206126,0.0001425097,0.00005580627,0.0003860395,0.00006094835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006904221,0.001005575,0.1310795,0.0007236262,0.0003747098,0.001178352,0.0002012942,0.08809789,0.01488035,0.004082424,0.009750493,0.7479354],"study_design_scores_gemma":[0.00004368301,0.0003423565,0.0312106,0.0001635228,0.0001610811,0.0009817114,0.0003586095,0.9368009,0.01651596,0.007590271,0.005761497,0.00006975801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2800128,0.005640539,0.6940907,0.001686479,0.0005070161,0.0007260932,0.00328112,0.003519933,0.01053535],"genre_scores_gemma":[0.8199648,0.00122211,0.174377,0.0002421409,0.0001584758,0.0001741597,0.002321567,0.00002533827,0.001514433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003922499,"threshold_uncertainty_score":0.006136775,"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."}}