{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003867319,0.0001281692,0.0001474538,0.0001024343,0.003108948,0.00001081819,0.00006382762,0.000111687,0.0007324734],"category_scores_gemma":[0.00008937723,0.0001271578,0.00003517103,0.0004065053,0.00003679658,0.0001847175,0.000182365,0.001256671,0.000009001895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003536479,"about_ca_system_score_gemma":0.0001502676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608437,"about_ca_topic_score_gemma":0.001262052,"domain_scores_codex":[0.9978295,0.0008610066,0.0003201764,0.0003131537,0.0002343559,0.0004418048],"domain_scores_gemma":[0.999041,0.0003010531,0.0001157599,0.0001442011,0.0001483041,0.0002496459],"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":[0.0006136575,0.0000419797,0.7605603,0.00006064261,0.00001231828,0.000006328479,0.002663318,0.2279286,0.00005886016,0.001465703,0.003400821,0.003187519],"study_design_scores_gemma":[0.00007695356,0.0001403895,0.03071118,0.00001843949,0.00001967047,0.000006553618,0.006128337,0.9598563,0.000001996813,0.0005413441,0.002394859,0.0001039427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700214,0.0008771535,0.01880732,0.005072611,0.002509492,0.001818027,0.00006278056,0.0003994313,0.0004318186],"genre_scores_gemma":[0.9949639,0.00001120673,0.0004379406,0.003319893,0.0006149475,0.0001931297,0.0000316186,0.00002670881,0.0004006054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7319278,"threshold_uncertainty_score":0.9981889,"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."}}