{"id":"W3162841855","doi":"10.18280/isi.260210","title":"A Diabetic Prediction System Based on Mean Shift Clustering","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Bayes' theorem; Naive Bayes classifier; Diabetes mellitus; Body mass index; Artificial intelligence; Mean-shift; Medicine; Computer science; Machine learning; Internal medicine; Pattern recognition (psychology); Endocrinology; Bayesian probability","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00100797,0.000214177,0.0002993465,0.0002630271,0.001361468,0.00007862679,0.0001629234,0.0003190258,0.0002300469],"category_scores_gemma":[0.0007613336,0.0002143287,0.00008648186,0.0005221611,0.00007196645,0.001134752,0.00008383887,0.0005431393,0.001350946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637359,"about_ca_system_score_gemma":0.0005910255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004864924,"about_ca_topic_score_gemma":0.0005831757,"domain_scores_codex":[0.9967645,0.0006006065,0.00134591,0.0002163969,0.0004722977,0.0006002637],"domain_scores_gemma":[0.9977148,0.0004854391,0.0004624386,0.0005047177,0.0006476484,0.0001849734],"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":[0.0009604256,0.0002622176,0.3657048,0.04642405,0.0001742041,0.0001046048,0.2636789,0.08632331,0.0005959716,0.04839595,0.004536954,0.1828385],"study_design_scores_gemma":[0.0005913874,0.0002709874,0.02906419,0.006698259,0.00004323194,0.0000125377,0.05597607,0.8982608,0.001701334,0.001217805,0.005705318,0.000458126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7613014,0.0001109961,0.1433018,0.0007835482,0.006130783,0.002408518,0.000255501,0.001583815,0.08412368],"genre_scores_gemma":[0.9971693,0.000006482684,0.0006745903,0.00109564,0.0003197179,0.000376658,0.0002480037,0.00002709828,0.00008250889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8119375,"threshold_uncertainty_score":0.9999386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05846500220636072,"score_gpt":0.3479139689302561,"score_spread":0.2894489667238953,"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."}}