A Data Mining Technique Combining Fuzzy Sets Theory and Bayesian Classifier - An Application of Auditing the Health Insurance Fee for the National Health Insurance

• Main Authors: Chung-Hsien Lan, 藍中賢
• Other Authors: Chien-Lung Chan
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/81527147819753904650

碩士 === 元智大學 === 資訊管理研究所 === 88 === Information technology is widely applied in business from traditional electronic data processing to cutting-edged decision support systems, data mining and data warehouse today. To analyze historical data effectively and to build the problem solving rules, data mining techniques are used to reduce the cost and time for data analysis. Many data mining techniques can be used to solve “classification” problems, especially the Bayesian classifier. Bayesian classifier based on Bayesian inference, because it can take all attributes affecting the classification result into account. In addition, the Bayesian classifier is used to judge the class of a case clearly and explain the result in detail. However, when dealing with continuous attributes, Bayesian inference needs to integrate different probability distributions. In this case, the computation is very complex. Therefore, the research transforms the continuous attributes with the Fuzzy sets theory by converting continuous attributes to discrete. Consequently, a new data mining models by incorporating the Bayesian classifier and the Fuzzy Set Theory can be constructed. The prototype based on the Fuzzy Bayesian classifier fulfills the purpose of data mining technique --- transform data to information, and transform validated information into knowledge. Hopefully, Fuzzy Bayesian classifier can be used to analyze huge amount of data and to find the hidden rules effectively. For the application, Fuzzy Bayesian classifier is used to analyze a portion of health insurance fee data for validating the decision model granting the application fee. In this case, the sensitivity is 0.639 and the specificity is 0.968. Therefore, the effectiveness of this model is supported. In the end, this system can be applied to audit the health insurance fee and control the increasing speed of health insurance fee.