{"id":"W1603920668","doi":"10.1109/meco.2015.7181948","title":"An intelligent system for diabetes prediction","year":2015,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l'Économie, de la Science et de l'Innovation - Québec; Heart and Stroke Foundation of Canada","keywords":"Computer science; Support vector machine; Naive Bayes classifier; Reliability (semiconductor); Machine learning; Process (computing); Artificial intelligence; Bayes' theorem; Data mining; Outcome (game theory); The Internet; Joint (building); Bayesian probability; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001402043,0.0001151149,0.0001911732,0.00005936203,0.0004725131,0.00001017764,0.0001813134,0.0002181389,0.0001082907],"category_scores_gemma":[0.0003654565,0.00009456641,0.00004234863,0.0001257645,0.00003218533,0.0001944705,0.00003493969,0.0002326868,0.0007696912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005690824,"about_ca_system_score_gemma":0.0003336266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006566464,"about_ca_topic_score_gemma":0.0008741579,"domain_scores_codex":[0.998019,0.0003358061,0.0006449582,0.0002728418,0.0002058045,0.0005216062],"domain_scores_gemma":[0.9979462,0.0003856995,0.0001293877,0.0003757484,0.0007359015,0.0004270401],"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":[0.0001736341,0.0001356748,0.850292,0.001431927,0.00003089064,0.000001252468,0.01631731,0.0004377502,0.0007309706,0.07167576,0.04153046,0.01724237],"study_design_scores_gemma":[0.0006222901,0.00251126,0.003469176,0.0009320622,0.00007090018,0.000001525336,0.3249203,0.4753909,0.02490196,0.01197337,0.154557,0.000649207],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9477498,0.0001288941,0.029866,0.001727774,0.006787512,0.003512553,0.0001104729,0.001181477,0.008935509],"genre_scores_gemma":[0.9946697,0.000003748136,0.001502248,0.0007339671,0.001301255,0.0008414854,0.00005617676,0.00003196563,0.0008594341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8468229,"threshold_uncertainty_score":0.9893079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3119049866439406,"score_gpt":0.5105526515381021,"score_spread":0.1986476648941615,"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."}}