Information regarding the function of HVA will be incomplete if some patients do not experience any HVA dysfunction for the duration of this study. Hence,techniques for censored and truncated data will be used to deal with the incomplete data. Regressions and appropriate survival analysis will be conducted to capture the distribution of the function of HVA, and to identify the independent predictors of HVA function. In this study, a novel hybridizing data mining techniques will be applied to reduce data uncertainty. The HD-index that was developed by Prof. K. Y. Huang will be used to filter out uncertain data and to improve the precision of the forecasting model.