## 4 Discussion

Table 3: Experimental results of MCI conversion prediction using different scenarios. In this study, we developed an MCI conversion prediction framework based on 3D-ZM and IDPSO algorithms. Basically, voxel-based feature extraction methods suffer from the curse of dimensionality. As a result, a stage of feature selection or feature reduction is required in the development of a robust prediction system. In addition to the voxel-based method, other feature extraction techniques have been widely used in the area of AD classification such as contourlet transform (CoT) [54], complex wavelet transforms (CWT) [55], discrete wavelet transform (DWT) [56], Shearlet transforms (ST) [57], moment methods [58], and histogram-based features generated from individual connectivity networks [15]. In this study, we extracted the statistical features from GM images using the 3D-ZM algorithm. The 3D-ZM algorithm is a highly effective statistical technique for capturing the shape, texture, and symmetry information of anatomical structures in 3D-MRI scans. One key advantage of using 3D-ZM over 2D ones is that they provide a more comprehensive representation of an object's 3D shape. These moments allow for the extraction of a wide range of features from sMRI images, including measures of symmetry, such as the degree of asymmetry between left and right brain hemispheres, and measures of shape complexity, such as the number of protrusions or indentations in a brain structure. In addition, the 3D-ZM method can represent a low dimensional informative statistical pattern from the 3D-MRI scan. For example, our 3D-ZM method generated 372 statistical features from a 3D GM image with size of 121 \(\times\) 145 \(\times\) 121 voxels. Thus, the computational cost of the classification task can be significantly reduced by applying a 3D-ZM feature extraction which also improves the performance of classifiers. Our experimental results showed that a feature extraction based on the 3D-ZM algorithm can provide better features for MCI conversion prediction than the raw voxel value (see Table 3). However, some irrelevant features may still be included in the extracted set, thus increasing the complexity and decreasing the accuracy of classification. The main reason for applying feature selection is to decrease the dimensionality of the problem by eliminating ineffective features and enhancing classifier performance. This can be challenging due to the considerable number of features and the nonlinear relationship between accuracy and the number of features. In fact, selecting the most effective feature is considered a nonlinear optimization problem which can be solved by metaheuristic algorithms. These algorithms can intelligently search among all possible solutions to the problem and find the optimum one. Furthermore, they have shown a high ability to solve multi-objective optimization problems. Metaheuristic algorithms are particularly suitable for solving feature selection problems because they offer easy control over parameters. To date, only a few studies have employed metaheuristic algorithms in AD detection. For example, [PERSON] and peers proposed a robust feature-selection method for AD classification and MCI conversion prediction on the basis of feature ranking and a GA algorithm [47]. In the case of sMRI data analysis, the PSO algorithm has been shown to be particularly effective at identifying features associated with the conversion from MCI to AD (Table 3). In fact, the PSO algorithm is known as a powerful algorithm among all available metaheuristic methods. The key advantage of the PSO algorithm is its ability to search large feature spaces efficiently and effectively. The PSO algorithm achieves this by maintaining a population of candidate solutions, each of which represents a subset of features, and iteratively updating the positions of these solutions based on their fitness (i.e., their ability to predict the outcome variable accurately).