특징점 선택방법과 SVM 학습법을 이용한 당뇨병 데이터에서의 당뇨병성 신장합병증의 예측Prediction of Diabetic Nephropathy from Diabetes Dataset using Feature Selection Methods and SVM Learning
- Other Titles
- Prediction of Diabetic Nephropathy from Diabetes Dataset using Feature Selection Methods and SVM Learning
- Authors
- 조백환; 이종실; 지영준; 김광원; 김인영; 김선일
- Issue Date
- Jun-2007
- Publisher
- 대한의용생체공학회
- Keywords
- diabetic nephropathy; feature selection; support vector machine; cost-sensitive learning
- Citation
- 의공학회지, v.28, no.3, pp.355 - 362
- Indexed
- KCI
- Journal Title
- 의공학회지
- Volume
- 28
- Number
- 3
- Start Page
- 355
- End Page
- 362
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179926
- ISSN
- 1229-0807
- Abstract
- Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.
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