| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系 === 105 === Due to increased number of aging population and population structure changes, people are concerned about aging-related problems that put great burdens and make enormous impacts on the family, society, and nation. Aging population brings along chronic disease, deterioration of boding function and disabilities resulting in increase medical expenditure and heavy burden to the family, society and the nation as a whole. Regular medical checkup and early treatment is the most effective solution to this problem. Important but non-intuitive information and knowledge in the adult health check-up database can be found, extracted and organized using big data mining. This thesis applied big data mining techniques with Rapidminer to enhance understanding of abnormal item combinations in adult health check-up data. Researchers collected a total of 1,158 health examination records. Data analysis consisted of the two parts of general statistics by SPSS and big data mining with Rapidminer. General statistics was a pre-process used to clarify and organize data in preparation for big data mining. A decision tree is proposed to solve the classification problem with large number of classes and continuous attributes. This research uses the decision tree to explore abnormal item combination on community health screening services for the elderly. Besides, related factors of the information and knowledge are also discussed. Suggestions may serve as a useful reference for doctor to improve the correct diagnosis of a chronic illness.
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