关键词: 空腹血糖, 健康档案, 主成分分析（PCA）, 核主成分分析（KPCA）, 最小二乘向量机（LSSVM）

Abstract: Diabetes, a preventable and controllable chronic disease, will cause a lot of complications, which is harmful to people’s health. Therefore, the early diagnosis of diabetes and the intervention of lifestyle are very necessary to the prevention of chronic diabetic complications. This paper makes use of the data of electronic health records to predict the level of fasting blood glucose, which is an important basis of early diagnosis and intervention. However, the data of health records have such features as multidimensional, noise interference, strong coupling and nonlinear. Therefore, this paper proposes a method based on the combination of KPCA and LSSVM, and makes a comparison of the three models which are LSSVM, PCA-LSSVM and KPCA-LSSVM. The results show that the accuracy is significantly improved through KPCA-LSSVM, the integral area of ROC curve is also close to 1, which proves the KPCA-LSSVM to be an appropriate method to the prediction of the level of fasting blood glucose. More importantly, it provides a new reference method for medical data mining.

Key words: fasting blood glucose, health record, Principal Component Analysis（PCA）, Kernel Principal Component Analysis（KPCA）, Least Squares Support Vector Machine（LSSVM）