## 4 Discussion
Therefore, it is considered a suitable method to use with sufficient preprocessing, such as adding interaction effects, and additional feature engineering; the individual features should be sufficiently reviewed by the practitioner. Computer-aided diagnosis and treatment expert systems using multimodal data have been proposed to manage various chronic diseases owing to the increase in the aging population. [PERSON] et al. provided a detailed summary of how to combine different related knowledge to make an important decision in a multimodal data-driven approach for a smart healthcare system [37]. Multimodal fusion is largely divided into three types: early, medium-term, and late fusion. Early fusion refers to an array of methods that directly combine features before inputting them to a predictive system [38]. This method can train a predictive system using various types of knowledge and can lead to a model that makes comprehensive decisions on multimodal data. In medium-term fusion, the model receives multiple data and individually processes each data point within the model, which is mainly implemented by neural network-based algorithms, and then combines each feature set [39]. Finally, late fusion trains a universal model for each data source and directly integrates all the inferences made by individual models. In this study, to achieve the task of classifying patient groups from HC, only the early fusion method was considered. In the case of the medium-term fusion method, it is important to create a good latent representation by a neural-network-based model, and to build a reliable model, a sufficient dataset is required. In terms of the advantages of late fusion, it is possible to avoid model complexity and training difficulty owing to higher dimensions in feature-level fusion approaches, combine models without retraining, and scale up easily [40]. In our study, late fusion was not considered because the input features were obtained through sufficient image preprocessing steps, and we expected that those features for each phase would only express the state of each phase for a representative brain region. However, we will consider the medium-term or late fusion method for complex data analysis, such as multiple modalities or imaging data, as well as an additional dataset in future studies. This study has the following limitations and considerations for future work. First, the amount of data used in our experiment may be insufficient or biased to generalize the performance of the model. In future research, it will be necessary to secure sufficient samples and separately secure external data or data for verification. Second, in the feature selection section, we only considered the feature selection algorithm as a filter-based method, especially the feature ranking method, despite many opportunities to use diverse feature selection techniques in medical data analysis [16]. One of the benefits of the feature ranking method is that these methods are independent of the learning algorithm and require less computational time than wrapper methods. We expected that these properties could be used to avoid making our learning model overfitted under a limited number of samples and to observe the significant regions to distinguish the AD patient group from the control group. We could reproduce our experiment with an additional or external dataset. Third, because only limited information (quantitative analysis result of dual FBB) was used, and the classification was performed using the feature selection method, it is impossible to explain the changes in the brain as a whole. Changes in beta-amyloid deposition appear locally or globally in the cortical areas. In the future, we propose to reduce the time consumption of the quantitative analysis process and analyze the entire brain region by using the brain image of amyloid PET as it is for data analysis.