# Dementia Prediction Support Model Using Regression Analysis and Image Style Transfer
## 5 Conclusions  
This study proposed a prediction support model for dementia based on regression analysis using image style transfer. The proposed method was composed of three steps. First, MRI data and medical information data from patients with dementia were collected and pre-processed. In the second step, through the medical information data, the features of the factors affecting dementia were extracted. Through a regression analysis, the factors affecting dementia and their impacts on it were observed. The factors affecting dementia extracted as a result of the regression analysis are alcohol addicted brain, aging brain, cerebral hemorrhage, and traumatic brain injury. Accordingly, the images of the factors affecting it were recollected. In the final step, the backgrounds of all images used in the analysis were removed to resolve the problem of transferring unnecessary features. Accordingly, the images related to the factors affecting it were transferred to the images of the MRI normal brain. Thus, the normal brain was transformed into a patient's brain through the style of the factors affecting it. For performance evaluation, the similarities were compared between the brain images of very mild cemented, mild cemented, and middle dementia stages and the style transferred brain image. In order to evaluate this performance, a comparison of similarities and features between the style transmitted image and the dementia stage, and a performance comparison with the existing method were conducted. As a result of the similarity comparison, when the properties of the alcohol brain were transmitted, on average, the results were most similar to those of dementia. In addition, in the case of aging brains and alcohol brains, features similar to those of dementia were extracted most. In addition, the proposed method was excellent in its similarity assessment rate to the existing methods. Thus, the proposed method provides the results necessary to predict or prevent dementia through changes in the brain. In addition, changes can be observed by transferring the styles of each influencing factor to images, such as joints and cancers, in the form of image data.  
Figure 10: Graph of the average similarity of the existing method and the proposed method according to the stage of dementia.  
However, the proposed method proceeded with a limited assumption, due to a lack of public data. Since the brain has been transformed through the most fundamental factors, there are limitations on the information that has been studied in a small number of cases and provided to patients or medical staff. Further, since the proposed model is in a black box format, there would be a limitation that Alzheimer's disease risk information may be predicted through the result of style transfer. In addition, it takes much time and computational cost to empirically choose parameters through repeated experiments. In the future, in order to improve the amount of computation, accuracy, and performance used in MRI image analysis, the MRI data will be restructured and pre-processed through Realign, Segment, Normalize, and Smooth. In addition, in order to provide reliable information on [PERSON]'s disease, the relationship between the influencing factors and the basis for selecting the parameters of the model will be additionally studied to explore a white box-based auxiliary model.  
Conceptualization, J.-W.B.; methodology, J.-W.B. and K.C.; validation, J.-W.B. and K.C.; writing--original draft preparation, J.-W.B. and K.C.; writing--review and editing, J.-W.B. and K.C.; visualization, J.-W.B. and K.C.; supervision, K.C.; project administration, K.C.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Kyonggi University Research Grant 2021.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.