{"id":"W2998499297","doi":"","title":"Comparative Analysis of Features Selection Techniques for Classification in Healthcare.","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Data Mining in Pattern Recognition","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Purdue Pharma (Canada)","funders":"","keywords":"Computer science; Selection (genetic algorithm); Health care; Artificial intelligence","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":[0.001899928,0.0001143462,0.0004118304,0.0006932782,0.0001571166,0.000009193631,0.0001172504,0.0001877886,0.00006635842],"category_scores_gemma":[0.0004195069,0.0001152792,0.00002656311,0.000691566,0.00002514135,0.0001853207,0.00007386703,0.0006407458,0.000009373372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009452785,"about_ca_system_score_gemma":0.00005965012,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01454954,"about_ca_topic_score_gemma":0.05195276,"domain_scores_codex":[0.9976239,0.0009167048,0.0006537821,0.0004379004,0.0001185572,0.0002491268],"domain_scores_gemma":[0.9978481,0.001353345,0.00040255,0.0001958032,0.0001627267,0.00003746968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009102831,0.00002684007,0.8509957,0.0002610613,0.00002458835,1.78291e-7,0.002740133,0.00002929655,0.0002980018,0.000009915313,0.00003201363,0.1454913],"study_design_scores_gemma":[0.0002063948,0.000211786,0.529047,0.0005732937,0.00006464994,5.661492e-7,0.006308286,0.4627513,0.0001996042,0.0001316971,0.000366065,0.0001393957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930898,0.0001479574,0.004232619,0.0007934651,0.0001151926,0.001013573,0.0003440754,0.00005591847,0.0002073962],"genre_scores_gemma":[0.9927691,0.0001154667,0.002156212,0.0001736648,0.0000466102,0.0001496718,0.004525627,0.00001170419,0.00005200694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.462722,"threshold_uncertainty_score":0.9920127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2409741547271774,"score_gpt":0.5102778579546496,"score_spread":0.2693037032274722,"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."}}