{"id":"W4388443664","doi":"10.18280/isi.280407","title":"Feature Importance Analysis for Glucose Level Detection in Type 2 Diabetes using Machine Learning","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Logistic regression; Receiver operating characteristic; Artificial intelligence; Machine learning; Type 2 diabetes; Support vector machine; Body mass index; Computer science; Diabetes mellitus; Predictive modelling; Classifier (UML); Medicine; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515905,0.000171087,0.0003394994,0.0009600043,0.001058269,0.00004069247,0.0001324154,0.0003117853,0.00004669622],"category_scores_gemma":[0.002160068,0.000171222,0.00009866687,0.003369617,0.00004754775,0.001215925,0.00006271138,0.0005483057,0.0001541254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008027745,"about_ca_system_score_gemma":0.0001772315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376294,"about_ca_topic_score_gemma":0.008495872,"domain_scores_codex":[0.9979041,0.0002439325,0.0008369606,0.0001768089,0.0002166231,0.0006215501],"domain_scores_gemma":[0.9981181,0.0004901503,0.0005467436,0.0002082475,0.000553898,0.00008287063],"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":[0.0000918592,0.000006481834,0.9414456,0.0008763245,0.00007355253,9.958003e-7,0.01120097,0.01734123,0.001135178,0.0001502792,0.00007752253,0.02759994],"study_design_scores_gemma":[0.0001882852,0.00009068543,0.1182329,0.0002467696,0.00007091733,5.861004e-7,0.008776957,0.8649323,0.002814402,0.001432921,0.00297128,0.0002420159],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886476,0.0002100953,0.008824128,0.0001495165,0.000670079,0.0009991154,0.00005974335,0.000289059,0.0001506696],"genre_scores_gemma":[0.9977261,0.00008422564,0.000972922,0.0001809421,0.0001278287,0.0002100026,0.0005315514,0.00002246191,0.0001439296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8475911,"threshold_uncertainty_score":0.8139459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204048617906611,"score_gpt":0.3972895774949019,"score_spread":0.2768847157042408,"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."}}