{"id":"W2975651211","doi":"10.1109/iccse.2019.8845515","title":"Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting","year":2019,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Genetic programming; Computer science; Diabetes mellitus; Symbolic regression; Predictive modelling; Machine learning; Voting; Genetic algorithm; Majority rule; Computation; Artificial intelligence; Data mining; Medicine; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001182037,0.0006466398,0.0009869895,0.0007390066,0.0003697431,0.0008664007,0.0009523088,0.0007870552,0.0007945922],"category_scores_gemma":[0.002521091,0.000259888,0.000790723,0.0006816825,0.0002610131,0.0005568636,0.0005674768,0.0008555166,0.0001206098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005668398,"about_ca_system_score_gemma":0.0008285855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007320235,"about_ca_topic_score_gemma":0.005501481,"domain_scores_codex":[0.9995965,0.0001509843,0.00001906617,0.00009367209,0.00007995551,0.00005977521],"domain_scores_gemma":[0.9990709,0.0006081865,0.00008911522,0.00003519636,0.0001608515,0.00003566889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008859933,0.0000948169,0.003988866,0.0000309537,0.00005934945,0.00006813408,0.00002626811,0.9231661,0.0006884013,0.001453348,0.0005770527,0.06975816],"study_design_scores_gemma":[0.00000276468,0.00001487292,0.0001779619,0.000002543996,0.000004574113,0.000004217818,0.000004529069,0.9991044,0.000128561,0.0005136201,0.00004031544,0.000001557815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3592355,0.0008910383,0.6331704,0.00109085,0.0001142139,0.0001176966,0.0002655852,0.0004203071,0.004694388],"genre_scores_gemma":[0.9266331,0.0001896414,0.07112802,0.0001487319,0.00003478603,0.00007087065,0.0002442035,0.00002203899,0.001528753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007320235,"threshold_uncertainty_score":0.01455522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376826004219372,"score_gpt":0.2396370995375081,"score_spread":0.2258688394953144,"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."}}