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 ‎Cardiovascular diseases (CVDs) are the leading cause of death worldwide‎. ‎To specify an appropriate model to determine the risk of CVD and predict survival rate‎, ‎users are required to specify a functional form which relates the outcome variables to the input ones‎. ‎In this paper‎, ‎we proposed a dimension reduction method using a general model‎, ‎which includes many widely used survival models as special cases‎.

‎Using an appropriate combination of dimension reduction and Cox Proportional Hazards model‎, ‎we found a method which is effective for survival prediction‎.  

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The ordinary linear regression model is $Y=Xbeta+varepsilon$ and the estimation of parameter $beta$ is: $hatbeta=(X'X)^{-1}X'Y$. However, when using this estimator in a practical way, certain problems may arise such as variable selection, collinearity, high dimensionality, dimension reduction, and measurement error, which makes it difficult to use the above estimator. In most of these cases, the main problem is the singularity of the matrix $X'X$. Many solutions have been proposed to solve them. In this article, while reviewing these problems, a set of common solutions as well as some special and advanced methods (which are less favored by someone, but still have the potential to solve these problems intelligently) to solve them.