{"id":"W4214817333","doi":"10.1109/icaiic54071.2022.9722652","title":"Heart Disease Prediction Using Adaptive Infinite Feature Selection and Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Feature selection; Artificial intelligence; Artificial neural network; Machine learning; Multilayer perceptron; Heart disease; Feature (linguistics); Test data; Deep learning; Selection (genetic algorithm); Blood pressure; Pattern recognition (psychology); Data mining; Internal medicine; Medicine","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":[0.001517441,0.0006505331,0.0009435491,0.001290145,0.0002416474,0.0004841573,0.0007942221,0.0005750094,0.0006353018],"category_scores_gemma":[0.002394035,0.0002347254,0.0007197339,0.0008719346,0.0002293886,0.0006353499,0.0006453813,0.0006696826,0.0001832848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00053567,"about_ca_system_score_gemma":0.0005789999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007633193,"about_ca_topic_score_gemma":0.008555107,"domain_scores_codex":[0.9993744,0.000212655,0.0000567715,0.0001381333,0.0001181132,0.0000998613],"domain_scores_gemma":[0.9989439,0.0005526274,0.0001122346,0.00009273357,0.0002347931,0.0000638295],"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":[0.001147085,0.0009350499,0.05939088,0.0001006498,0.0003868927,0.000536925,0.00007130553,0.4801676,0.006473128,0.001177109,0.007803621,0.4418098],"study_design_scores_gemma":[0.00001561443,0.00004666215,0.003145373,0.000005676525,0.00001303366,0.00002970245,0.0000054899,0.9953312,0.0005615852,0.0006954859,0.0001434376,0.000006696483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5437239,0.002737826,0.4470941,0.001151604,0.0002122724,0.00008674028,0.001433289,0.002033915,0.001526287],"genre_scores_gemma":[0.961935,0.0002478556,0.03538003,0.000165823,0.0000824167,0.00004644556,0.001368786,0.00001840593,0.0007551793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007633193,"threshold_uncertainty_score":0.01517749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1200845977623338,"score_gpt":0.4227805782276293,"score_spread":0.3026959804652955,"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."}}