本文使用資料萃取方法判辨健保費用異常醫療診所的查核問題，本研究以大臺北地區、金門縣、連江縣執業之中醫、西醫、牙醫基層診所為標的，以資料萃取方法偵測醫療院所申報門診費用時是否有造假。本文應用類神經網路、區別分析、邏輯迴歸分析的資料萃取法評估辨認異常的案例，從比較各種不同方法判別正、異常醫療院所的結果，發現中醫與西醫診所適用於監督型類神經網路模式、牙醫部份則以邏輯迴歸分析結果最優。本研究的預測模式可用於輔助健保稽核者判斷可能異常之基層診所，以改善健保財務浮濫或虛報醫療費用情形。未來此預判模式可擴充至不同地區別、不同業務別，或建構更細緻的資料萃取模式。

　　　　　This study use data mining to detect assessment issues for hospitals with abnormal charge onhealth care insurance. We select dispensaries of Chinese medicine, western medicine, and dentistmainly located at Northern Taiwan as the studying objects, and use data mining to detect theirrequested outpatient charges to see is there any falseness existed. We explore data mining byusing neural network, discriminant analysis, and logistic regression to evaluate and recognize theabnormal cases. Comparing the results assessing by different methods, we find that neuralnetwork model can monitor hospitals of Chinese medicine, western medicine while logisticregression is more suitable for dentist. This prediction model can assist the auditors to check thepossible abnormal hospitals in order to improve the overflow and untrue charge on healthy careinsurance. This model can also be extended to different areas, professions, or be advanced formore specified data mining model.