{"id":"W4200513061","doi":"10.18280/isi.260604","title":"Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Badan Riset dan Inovasi Nasional","keywords":"Feature selection; Computer science; Normalization (sociology); Artificial intelligence; Context (archaeology); Feature (linguistics); Selection (genetic algorithm); Machine learning; Predictive modelling; Data mining; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183299,0.0009220901,0.0008126457,0.0009795704,0.0004452826,0.00105929,0.0008957436,0.0006546617,0.001618384],"category_scores_gemma":[0.00142282,0.0002386368,0.0009065929,0.000669449,0.0003028105,0.0006630556,0.0005337211,0.000538248,0.0005824732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000810339,"about_ca_system_score_gemma":0.00111219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142121,"about_ca_topic_score_gemma":0.006563596,"domain_scores_codex":[0.999134,0.0002141549,0.0000538346,0.0002318178,0.0002340151,0.0001321901],"domain_scores_gemma":[0.9994581,0.000143995,0.00004506801,0.00003606263,0.0002891636,0.00002759056],"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":[0.0007425722,0.0006277942,0.01984899,0.0002273405,0.0004978718,0.0005024056,0.0002691612,0.4951141,0.01969186,0.006972043,0.00638562,0.4491204],"study_design_scores_gemma":[0.00001133448,0.0001498488,0.00323234,0.000007752475,0.00004906172,0.00007740902,0.00001471341,0.9928329,0.001719936,0.001168009,0.0007200007,0.00001667238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07840289,0.0007231324,0.9142817,0.0006533951,0.00009140129,0.0003394952,0.000360103,0.002470786,0.002677105],"genre_scores_gemma":[0.884152,0.0003301963,0.1096977,0.0001777848,0.0000771669,0.0003861809,0.0007137198,0.00003616343,0.004429105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01142121,"threshold_uncertainty_score":0.02270943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09604406356798188,"score_gpt":0.39403385238368,"score_spread":0.2979897888156982,"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."}}