{"id":"W2173730923","doi":"10.5815/ijisa.2015.12.08","title":"Heart Diseases Diagnosis Using Neural Networks Arbitration","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems and Applications","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Support vector machine; Heart disease; Artificial intelligence; Artificial neural network; Machine learning; Multilayer perceptron; Medical diagnosis; Set (abstract data type); Perceptron; Feedforward neural network; Medicine; Cardiology; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.000657897,0.0001387895,0.0002734153,0.0001988928,0.0002734625,0.00006452043,0.0003049287,0.0001230229,0.00004395961],"category_scores_gemma":[0.0002479309,0.0001181283,0.00009259517,0.0001622946,0.00007538604,0.0002794471,0.00007447305,0.0004178467,0.00003086464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003353098,"about_ca_system_score_gemma":0.0002610595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009955496,"about_ca_topic_score_gemma":0.00007573691,"domain_scores_codex":[0.9975644,0.0002653384,0.001255361,0.0001746696,0.0005042044,0.000236056],"domain_scores_gemma":[0.9961178,0.0004966293,0.0007654538,0.0001633937,0.0020923,0.0003644612],"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":[0.0001928432,0.0003051453,0.7948474,0.0001067241,0.0002069161,0.00002135631,0.001918376,0.1469508,0.00008677201,0.03626559,0.007908907,0.01118922],"study_design_scores_gemma":[0.0005283143,0.0002885442,0.005000878,0.0009785683,0.0001533787,0.0003145014,0.02327053,0.734934,0.0001667453,0.005180378,0.2287005,0.0004836729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5927656,0.01246172,0.3761723,0.005238251,0.009888259,0.002425688,0.0001207985,0.00009257994,0.0008348715],"genre_scores_gemma":[0.9948637,0.0003140843,0.0003106656,0.0006217827,0.003566169,0.0002104828,0.00001503315,0.00002049484,0.00007759965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7898465,"threshold_uncertainty_score":0.4817134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1966711323055732,"score_gpt":0.4811983888718895,"score_spread":0.2845272565663163,"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."}}