# Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
## 3 Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
### Dementia Influencing Factors Extraction Using Regression Analysis  
Dimension reduction and feature selection may enhance the prediction performance. To do so, conditions affecting dementia are extracted through regression analysis. Regression analysis extracts the independent variables that can best explain the dependent variables by modeling the relationships among the variables. There are various types of regression analyses, including basic linear regression analysis, multiple linear regression analysis, logistic regression analysis, LASSO regression analysis, etc. Regression analysis has the limitation that predictability decreases while variability increases, if there are too many variables or the correlation between variables is high. To resolve this problem, the number of variables is reduced, or the predicting model is normalized [20]. If logistic regression analysis is used, there is a disadvantage that reduces the accuracy of the classification of unnecessary features. In addition, if the dimension of test data is larger than the number of samples of training data, the problem of overfitting may occur. Data on [PERSON]'s disease have a small number of samples and various features. To enhance the accuracy of the prediction of [PERSON]'s disease, it is necessary to choose features with the best explanation power. The LASSO (Least Absolute Shrinkage Selector Operator) regression analysis is a linear regression method with an L1-norm penalty. This applies the same level of normalization regardless of the size of the parameters. Normalization can perform characteristic selection and model learning simultaneously. Accordingly, it has advantages that it is possible to create a model simply by creating parameters with a small value being created as 0 or a value approximate to 0 by removing the relevant variables from the model, and it is useful for interpretation. The L1-norm refers to the sum of the absolute values of vector elements. It is possible to understand changes in factor value. In addition, it is possible to resolve the complexity of the model [21]. However, logistic and LASSO can be used if \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject ID** & **MRI ID** & **eTIV** & **nWBV** & **ASF** \\ \hline OAS2\_0001 & OAS2\_0001\_MR1 & 1987 & 0.696 & 0.883 \\ OAS2\_0001 & OAS2\_0001\_MR2 & 2004 & 0.681 & 0.876 \\ OAS2\_0002 & OAS2\_0002\_MR1 & 1678 & 0.736 & 1.046 \\ OAS2\_0002 & OAS2\_0002\_MR2 & 1738 & 0.713 & 1.01 \\ OAS2\_0002 & OAS2\_0002\_MR3 & 1698 & 0.701 & 1.034 \\ OAS2\_0004 & OAS2\_0004\_MR1 & 1215 & 0.71 & 1.444 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset of the information on Alzheimer patients. Figure 3: MRI data according to the dementia condition. binary classification is possible. Therefore, the data used in this study consist of more than two independent variables. Therefore, it uses multiple regression analysis as a regression analysis method. This removes the variables, such as 'Subject ID', 'MRI ID', 'MR Delay', 'SES', 'Group', and 'EDUC' which are unnecessary for analysis, and uses 'CDR', which indicates the clinical dementia rating as a dependent variable, and 'ASF', 'Age', 'MMSE', 'Visit', 'eTIV', and 'nWBV' as independent variables. Table 2 shows the result of the multiple regression analysis to extract the factors that affect dementia. \"Coef.\" refers to regression coefficient; \"Std. error\" refers to standard error; and \"\(t\)\" refers to \(t\)-test, and they are shown through the regression coefficient/standard error. This means that the smaller the standard error, the bigger the \(t\) value becomes, which means that there are high correlations between independent variables and dependent variables. Therefore, the bigger the \(t\) value, the more significant the results become. The equation \(p>|\,t|\) shows the \(p\)-value. Table 2 shows that MMSE has the highest correlation. MMSE is the method that is the most widely used dementia screening test, which simply evaluates cognitive function and dementia in a short time.