Applying Blue Ocean Strategy and Data Mining Technique to Analyze the Healthcare Industry and Medical Examination Database

• Main Authors: Yun-Yun Chang, 張筠昀
• Other Authors: Jenn-Long Liu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/46408501399086515373

碩士 === 義守大學 === 資訊管理學系 === 104 === Recently years, the change in the global population structure makes the nation toward an aged society. As a result, people increasingly concerns about their health such that the role of medical institutions also changes. The items or services of health examination become the new blue ocean for medical institutions. Accordingly, this thesis focuses on the study of Blue Ocean Strategy on the health evaluation center and takes the I-care Health Center as an example to proceed to the qualitative analysis under the framework of Blue Ocean Strategy. The dataset of medical examination, conducted by Nutrition and Health Survey in Taiwan from 2004 to 2008, is used in this work by excluding 1,827 samples and adopting 1,844 samples from 3,671 samples for the data mining analysis. Among the available samples, confirmed cases of cardiovascular disease are combined to 7 groups and analyzed using J48 algorithm, NB tree, Naïve Bayes, Bayes Net, and Multi-Layer Perceptron, respectively, which all have been implemented in the Weka software, and the accuracy of classification analyzed results are compared using confusion matrix. Experiments shown that the results of Group1 are acceptable when using all samples as the training set and ten-fold cross-validation. The best accuracy for the training set obtained using J48 is 81%, on the other hand, the performance of confusion matrix using ten-fold cross-validation is the best based on Bayes Net algorithm. Furthermore, this thesis adopts a fuzzy expert system to provide a personal evaluation interface for assessing the risk degree of cardiovascular disease and as a reference tool for self-health management and physician’s disease diagnosis.