{"id":"W2991899257","doi":"10.1007/s12553-019-00396-3","title":"New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection","year":2019,"lang":"en","type":"article","venue":"Health and Technology","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Feature (linguistics); Genetic algorithm; Heuristic; k-nearest neighbors algorithm; Data mining; Algorithm; Pattern recognition (psychology); Artificial intelligence; Optimization algorithm; Machine learning; Mathematical optimization; Mathematics","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":[0.0007018419,0.0007415776,0.001326308,0.001618363,0.0004650663,0.0008252127,0.000882299,0.0006571828,0.00190344],"category_scores_gemma":[0.001091004,0.0003084577,0.0009149414,0.001399999,0.0002337237,0.0006278732,0.0004779728,0.0004088582,0.0003687977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399969,"about_ca_system_score_gemma":0.0007327456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004374703,"about_ca_topic_score_gemma":0.002561576,"domain_scores_codex":[0.9994467,0.0001015739,0.00004848505,0.0001307209,0.0002203036,0.00005222897],"domain_scores_gemma":[0.9996388,0.0001306839,0.00003222888,0.00001841587,0.0001642737,0.0000156136],"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":[0.0002669542,0.0002505063,0.004508398,0.0002210999,0.0002865011,0.000263006,0.0001180433,0.2881434,0.02034582,0.003861658,0.005259927,0.6764746],"study_design_scores_gemma":[0.0000249588,0.00005564525,0.001057833,0.000007564329,0.00002898833,0.00009697062,0.00001257385,0.9955836,0.001535534,0.0006461475,0.0009398707,0.00001030841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02010443,0.0005787201,0.9772076,0.0001532025,0.00008014873,0.00008134081,0.00005950453,0.0005628145,0.001172219],"genre_scores_gemma":[0.5039496,0.0006225138,0.4890536,0.0002110546,0.0001785261,0.000471847,0.0004601209,0.00009757788,0.00495509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004374703,"threshold_uncertainty_score":0.008698523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06233590203800966,"score_gpt":0.461409309939189,"score_spread":0.3990734079011794,"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."}}