## 4 Results
### Training and Testing  
For the training setup, an 80:20 ratio between the training and test datasets was used. In this stage, 5 neuroimaging, 18 cognitive, and 19 combined highly weighted features from the proposed NCA-F were used to analyze the effect of these features on dementia prediction. AdB, ANN, SVM, and NB models are trained and tested, and the results are reported in Table 3. AdB has outperformed ANN, SVM, and NB models in most cases. For neuroimaging features, the SVM model outperformed the other three models, i.e., the ANN, AdB, and NB, and achieved ~74% classification accuracy, as shown in Table 4. SVM, ANN, and AdB models have performed almost equally, while NB has achieved the lowest performance measures as compared to other models, as shown in Figure 7a, which depicts the Receiver Operating Characteristic (ROC) curve for all four models on neuroimaging features. The Area Under the Curve (AUC) for SVM, ANN, and AdB models is ~80% whereas the NB AUC is ~74% after 10-fold cross-validation. For cognitive features, the AdB model has outperformed the remaining models mentioned and achieved ~83% classification accuracy. Figure 7b also shows that AdB has the optimum results with ~90% AUC. Similarly, for combined features, AdB has outperformed the other three models with ~83% accuracy and ~90% AUC. The combined features contain 7 neuroimaging and 12 cognitive features. From Table 4, it is evident that only neuroimaging features have not performed well for dementia prediction. Cognitive features are more effective than neuroimaging features for prediction. Combined features have also performed well for the prediction; however, the only problem with combined features is that neuroimaging features are required. Moreover, the AdB model has good performance results for all three features. We have explicitly checked the performance of all four models and concluded that AdB has the optimum results, as shown in Figure 7. For further investigations, the overall performance of all three features is checked for all four models, and the results of the AdB model are shown in Figure 8. Neuro, Cog, and Com are neuroimaging, cognitive, and combined features, respectively. Figure 9 also depicts that the ROC curve of cognitive and combined features has better results than neuroimaging features. Figure 6: A sample correlation matrix for neuroimaging features. Figure 7: _Cont_. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features Type** & **Features Number** & **Model** & \begin{tabular}{c} **Accuracy** \\ \end{tabular} & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline \multirow{4}{*}{**Neuroimaging**} & & \begin{tabular}{c} AdaBoost \\ ANN \\ SVM \\ \end{tabular} & 73.22 & 71.13 & 67.67 & 68.48 & 79.5 \\  & & \begin{tabular}{c} ANN \\ SVM \\ \end{tabular} & 73.83 & 71.80 & 68.44 & 69.28 & 80.14 \\  & & \begin{tabular}{c} SVM \\ NB \\ \end{tabular} & 74.33 & 72.54 & 69.07 & 69.95 & 79.06 \\  & & \begin{tabular}{c} NB \\ \end{tabular} & 63.72 & 66.94 & 67.82 & 63.62 & 74.72 \\ \hline \multirow{4}{*}{**Cognitive**} & & \begin{tabular}{c} AdaBoost \\ ANN \\ SVM \\ \end{tabular} & **83.15** & **82.56** & **79.76** & **80.80** & **90.03** \\  & & \begin{tabular}{c} ANN \\ SVM \\ \end{tabular} & 82.73 & 81.90 & 79.73 & 80.57 & 89.15 \\  & & \begin{tabular}{c} SVM \\ NB \\ \end{tabular} & 81.49 & 82.41 & 76.25 & 77.91 & 86.56 \\  & & \begin{tabular}{c} NB \\ \end{tabular} & 63.58 & 72.14 & 70.62 & 63.49 & 80.95 \\ \hline \multirow{4}{*}{**Combined**} & & \begin{tabular}{c} AdaBoost \\ ANN \\ SVM \\ \end{tabular} & **83.42** & **82.80** & **80.27** & **81.23** & **90.47** \\  & & \begin{tabular}{c} ANN \\ SVM \\ NB \\ \end{tabular} & 83.10 & 82.40 & 79.90 & 80.85 & 89.93 \\  & & \
\begin{tabular}{c} SVM \\ NB \\ \end{tabular} & 81.40 & 82.07 & 76.32 & 77.92 & 87.32 \\  & &
\begin{tabular}{c} NB \\ \end{tabular} & 66.97 & 72.38 & 72.64 & 66.97 & 81.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Features types and ML models comparison for ADNIMERGE dataset.