{"id":"W4388585148","doi":"10.18280/ijsse.130509","title":"Detection of Health Insurance Fraud using Bayesian Optimized XGBoost","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Environmental health; Computer science; Health insurance; Actuarial science; Computer security; Business; Risk analysis (engineering); Medicine; Artificial intelligence; Economics; Health care","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006551036,0.00006939108,0.0001720748,0.0003190222,0.00003628384,0.00003892493,0.0004076132,0.0000356254,0.000001490386],"category_scores_gemma":[0.0001041333,0.00007086587,0.00004710364,0.0002578967,0.00001681916,0.000549945,0.00008977239,0.000149881,4.80181e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007992076,"about_ca_system_score_gemma":0.00004731994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451081,"about_ca_topic_score_gemma":8.556809e-7,"domain_scores_codex":[0.9989957,0.00003025127,0.0004866956,0.00009500284,0.0002922546,0.0001000483],"domain_scores_gemma":[0.9992158,0.00007404832,0.0003482782,0.0001076749,0.0002086933,0.00004543525],"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":[0.0004289445,0.0002708226,0.003625381,0.0004397517,0.0006599792,0.0001494805,0.009735913,0.2510587,0.246531,0.06274112,0.0002048003,0.4241541],"study_design_scores_gemma":[0.0006399903,0.00007021728,0.01935717,0.0002716083,0.000002396029,0.0001973461,0.00004461488,0.9587121,0.01778929,0.001274886,0.00151163,0.0001287304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03380137,0.0001609915,0.9647642,0.0006324503,0.000506071,0.00004209626,0.00001490439,0.00006429021,0.00001361874],"genre_scores_gemma":[0.9530987,0.0006766579,0.04611962,0.00003533039,0.00005965081,5.732698e-7,0.000002751387,0.000005206122,0.000001533476],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9192973,"threshold_uncertainty_score":0.2889826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430264312689245,"score_gpt":0.2661878477067442,"score_spread":0.2518852045798518,"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."}}