New Method of Remedying Missing Values Based on Support Vector Regression Model

In the actual data there are a lot of missing values which can be properly remedied by other relevant factors in order to bring down the amount of missing information. In this paper Support Vector Regression (SVR) is applied to predict the values of abdominal circumference, body mass index and high density lipoproteins. After predicted by SVR, the average relative errors of abdominal circumference, body mass index and high density lipoproteins are respectively 4.39%, 5.73% and 11.08%, the mean absolute errors of abdominal circumference, body mass index and high density lipoproteins are respectively 3.55, 1.41 and 0.14, and the RMS errors of abdominal circumference, body mass index and high density lipoproteins are respectively 4.54, 1.8 and 0.18. Compared with other methods, the experimental results show that the mean prediction error of SVR is the smallest.