Applying Data Mining Techniques for Constructing Disease Risk Factor Analysis Model– The Case for Diabetic Nephropathy and Dialysis
碩士 === 輔仁大學 === 企業管理學系管理學碩士班 === 103 === Dialysis treatment has become a huge burden on national health insurance. Nephropathy is major factors used to diagnose whether diabetic patients start dialysis treatment. The purpose of this study is applying data mining techniques to analysis the databases...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/aatekn |
| Summary: | 碩士 === 輔仁大學 === 企業管理學系管理學碩士班 === 103 === Dialysis treatment has become a huge burden on national health insurance. Nephropathy is major factors used to diagnose whether diabetic patients start dialysis treatment. The purpose of this study is applying data mining techniques to analysis the databases of national health insurance to explore disease risk factors affecting diabetic patients without nephropathy start dialysis treatment in next three years. The proposed disease risk factor analysis model composes three data mining techniques including under sampling based on clustering (SBC), classification and regression tree (CART) and support vector machine (SVM). Experimental results showed that three disease risk factors involving “diabetes of over 5-years duration”, “Proliferative diabetic retinopathy”, and “vitreous hemorrhages” are selected as important risk factors by using the proposed analysis model. The diabetic patients with the three risk factors have higher incidences of dialysis than the diabetic patients without the three risk factors. The proposed model overcomes the class imbalance problem and can be used to accurately find important disease risk factors and high-risk groups.
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