{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007207295,0.0005968141,0.0006678225,0.001093536,0.0004553864,0.001159702,0.0008816335,0.0007170946,0.004917435],"category_scores_gemma":[0.002468818,0.0001891423,0.0004531939,0.0007695584,0.0001501775,0.0008597985,0.0006217258,0.0006503967,0.002738219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003916282,"about_ca_system_score_gemma":0.0006731941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003648153,"about_ca_topic_score_gemma":0.003143291,"domain_scores_codex":[0.9995524,0.00007450276,0.00004779384,0.0001564069,0.0001319122,0.00003697987],"domain_scores_gemma":[0.9994645,0.0002002314,0.00004796415,0.00008924543,0.0001602783,0.00003776422],"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":[0.001462494,0.0006012135,0.01726852,0.0004010547,0.0002319415,0.0007759286,0.000144052,0.02072735,0.01913233,0.004128832,0.04399907,0.8911274],"study_design_scores_gemma":[0.0002364132,0.0007034543,0.01917545,0.0001283338,0.0003945212,0.001518935,0.00009894072,0.8785867,0.02961094,0.01226826,0.0571485,0.0001295787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1200166,0.004102988,0.7522601,0.00219707,0.001407334,0.0007868471,0.008985519,0.09191815,0.01832527],"genre_scores_gemma":[0.6874802,0.001465817,0.2908353,0.001112723,0.0003733847,0.0004708264,0.008452856,0.0002321804,0.009576766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004917435,"threshold_uncertainty_score":0.01645046,"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."}}