## 4 Discussion  
Many recent studies have investigated new pattern recognition methods for identifying the MCI patients who are at risk of developing dementia due to AD. For example, the researchers in [47] developed a new feature selection technique based on feature ranking and a genetic algorithm (GA) to select the optimal features with maximum discriminative power and minimum numbers of selected features for AD detection. They used the Fisher criterion as part of the objective function in the GA to evaluate the feature subsets. The method was tested on 458 subjects from the ADNI dataset using 10-fold cross-validation and the achieved accuracy, sensitivity, specificity, and AUC were 75%, 76.92 %, 73.23%, and 0.75%, respectively. The authors in [48] proposed a scoring strategy based on linear discriminant analysis (LDA) for the multiclass diagnosis of AD using four different modalities and genetic features. The LASSO algorithm and principal component analysis (PCA) technique were used to remove irrelevant and interfering features before LDA. A binary extreme learning machine-based tree decision classifier was built for multiclass classification, and an accuracy of 66.7% and F1-score of 64.9% were achieved [48]. The sulcal features (depth, length, mean and Gaussian curvature, and surface area) were computed for 24 distinct sulci per subject from 210 subjects in the ADNI database using the Brain VISA sulcal identification pipeline [49]. The SVM was used for classification and 10-fold cross-validation was used to evaluate the performance. The classification accuracy was found to be 87.9%, the sensitivity 90.0%, and the specificity 86.7% based on 10 features and the area under the receiver operating characteristic curve was 0.89. In [14], the authors developed a computer-aided diagnosis CAD framework for early detection and prediction of AD and mild MCI conversion. The proposed CAD framework used a merit-based feature selection method and whole-brain voxel-wise analysis of baseline MRI data. The impact of different MRI spatial resolutions on AD classification and MCI conversion prediction was also explored. The proposed CAD framework yielded high classification accuracies in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm. Table 4 shows the detailed parameters of MCI conversion prediction performance using various machine learning approaches and sMRI data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **pMCI/sMCI** & \
\begin{tabular}{c} **Validation** \\ **Method** \\ \end{tabular} & **ACC (\%)** & **SEN (\%)** & **SPE (\%)** & **AUC** &
\begin{tabular}{c} **Conversion Time** \\ **(Months)** \\ \end{tabular} \\ \hline [PERSON] et al. (2020) [50] & 51/45 & 10-Fold & 75.87 & 73.71 & 77.51 & — & — \\ \hline [PERSON] et al. (2020) [51] & 189/245 & 5-Fold & 83.01 & — & — & — & 0–36 \\ \hline [PERSON] et al. (2017) [47] & 71/62 & 10-Fold & 75.00 & 76.92 & 73.23 & 0.75 & 0–36 \\ \hline [PERSON] et al. (2021) [48] & 110/208 & 5-Fold & 81.2\% & — & — & — & — \\ \hline [PERSON] et al. (2013) [52] & 97/93 & — & 69.00 & 66.00 & 72.00 & — & 0–36 \\ \hline [PERSON] et al. (2019) [14] & 112/102 & 10-Fold & 74.77 & 73.21 & 76.47 & 0.74 & 0–36 \\ \hline [PERSON] et al. (2018) [53] & 120/160 & 10-Fold & 72.08 & 75:11 & 71.05 & 0.71 & 0–18 \\ \hline Proposed Method & 105/121 & 10-Fold & 75.66 & 83.81 & 68.60 & 0.75 & 0–36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: State-of-the-artpMCI to sMCI prediction techniques using sMRI data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUC** \\ \hline Voxel-based & 50.88 & 46.67 & 54.55 & 0.51 \\
3 DZM-based & 53.10 & 50.48 & 55.37 & 0.523 \\ PSO-3 DZM-based & 68.58 & 71.43 & 66.12 & 0.69 \\ IDPSO-3 DZM-based & 75.66 & 83.81 & 68.60 & 0.76 \\ \hline \hline \end{tabular}
\end{table}