{"id":"W2575420350","doi":"10.1109/ftc.2016.7821702","title":"Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data","year":2016,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Machine learning; Multilayer perceptron; Artificial neural network; Field (mathematics); Data mining; Perceptron; Data classification; Structured support vector machine; Decision support system; Pattern recognition (psychology)","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":[0.00293855,0.0004322125,0.0005458317,0.001354714,0.0001956394,0.0007770687,0.0003498257,0.0006935159,0.0007498533],"category_scores_gemma":[0.008173726,0.0001503132,0.000456703,0.001237431,0.0002830896,0.0008420813,0.0004266188,0.000576167,0.0002042378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691759,"about_ca_system_score_gemma":0.0003728525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002838071,"about_ca_topic_score_gemma":0.001758027,"domain_scores_codex":[0.9979107,0.0008965619,0.0002163174,0.0001835501,0.0006585945,0.0001343489],"domain_scores_gemma":[0.9971942,0.001893353,0.0002288867,0.0001471117,0.0004756462,0.00006091041],"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":[0.0009749443,0.0004148024,0.02296726,0.0003484682,0.0003502168,0.0003024697,0.0002049999,0.2780392,0.007991166,0.003685206,0.002489553,0.6822316],"study_design_scores_gemma":[0.00001696613,0.0003017875,0.007376216,0.00005174322,0.00003858153,0.0001046898,0.0000756458,0.9845245,0.003533685,0.002887219,0.001064065,0.00002472747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.514451,0.0113115,0.4644037,0.001722828,0.0005405663,0.0001431775,0.0005690622,0.001250438,0.005607837],"genre_scores_gemma":[0.923361,0.001069616,0.07419031,0.00009586867,0.00008436338,0.00003941999,0.0002306093,0.00001277721,0.0009161088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00293855,"threshold_uncertainty_score":0.01554072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2208974463629566,"score_gpt":0.4974945472784253,"score_spread":0.2765971009154687,"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."}}