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研究生:黃子欣
研究生(外文):HUANG, TZU-HSIN
論文名稱:基於預期支付偏差和機器學習方法的醫療保險詐欺索賠偵測
論文名稱(外文):Fraudulent Medicare Claims Detection by the Expected Payment Deviations and Machine Learning Approach
指導教授:邱志洲邱志洲引用關係
指導教授(外文):CHIU, CHIH-CHOU
口試委員:高淩菁蔡榮發呂正欽邱志洲
口試委員(外文):KAO, LING-CHINGTSAI, JUNG-FALU, CHENG-CHINCHIU, CHIH-CHOU
口試日期:2022-06-22
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:經營管理系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:49
中文關鍵詞:醫療保險詐欺預期支付偏差機器學習
外文關鍵詞:Fraudulent Medicare ClaimsExpected Payment DeviationsMachine Learning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:94
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 5
2.1 醫療保險詐欺 5
2.1.1 醫療保險詐欺定義 5
2.1.2 醫療保險詐欺類型 5
2.2 詐欺偵測方式 7
2.2.1 詐欺辨識模型 8
第三章 研究方法 11
3.1 研究流程 11
3.2 廣義線性模型 12
3.3 多元適應性雲形迴歸 13
3.4 機器學習 15
3.4.1 分類與迴歸樹 16
3.4.2 隨機森林 17
3.4.3 混淆矩陣 19
第四章 研究結果 21
4.1 資料處理 21
4.1.1 資料來源 21
4.1.2 資料描述 22
4.1.3 資料預處理 25
4.1.4 資料分組 28
4.2 詐欺標籤定義與詐欺偵測模型建構 30
4.2.1 詐欺標籤定義 30
4.2.2 詐欺偵測模型建構 37
4.3 模型結果 38
4.3.1 模型比較 44
第五章 結論與建議 45
5.1 研究發現與結論 45
5.2 限制與建議 46
參考文獻 47

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10.Trnka, A., 2010, “Six sigma methodology with fraud detection”, Proceedings of the 9th WSEAS International Conference on Data Networks, Communications, Computers, Faro, Portugal, pp. 162-165.
11.Hu, J., Wang, F., Sun, J., Sorrentino, R. and Ebadollahi, S., 2012, “A healthcare utilization analysis framework for hot spotting and contextual anomaly detection”, AMIA Annual Symposium Proceedings, pp. 360-369.
12.Bauder, R. A. and Khoshgoftaar, T. M., 2016, “A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations”, 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), Pittsburgh, PA, USA
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14.Farbmacher, H., Löw, L., and Spindler, M., 2020, “An explainable attention network for fraud detection in claims management”, Journal of Econometrics, vol. 228, no. 2, pp. 244-258.
15.Bauder, R. A., & Khoshgoftaar, T. M., 2017, “Medicare Fraud Detection Using Machine Learning Methods”, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico
16.Ghuse, N., Pawar, P. and Potgantwar, A., 2017, “An Improved Approach for Fraud Detection in Health Insurance Using Data Mining Techniques”, Journal of Scientific Research in Network Security and Communication, vol. 5, no. 3, pp. 27-33.
17.Herland, M., Khoshgoftaar, T. M., and Bauder, R. A., 2018, “Big Data fraud detection using multiple medicare data sources”, Journal of Big Data, vol. 5, no. 1
18.Nelder, J.A. and Wedderburn, R.W.M., 1972, “Generalized Linear Models”, Journal of the Royal Statistical Society, Series A, vol. 135, no.3, pp. 370-384.
19.Friedman, Jerome H.,1991, “Multivariate Adaptive Regression Splines”, The Annals of Statistics, vol. 19, no. 1, pp. 1-67.
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