## 4 Results
### Comparative Analysis
#### 4.3.5 Overall Comparison  
The overall comparison between the proposed NCA-F and [PERSON] et al. [20] is given in Table 5. This table summarizes the results of the performance comparison in terms of the number of features used, the type of features, the dataset type, the number of records, the accuracy achieved, and the Area under the ROC curve (AUC). From Table 5, it can be observed that the proposed approach has achieved better performance on both datasets (ADNIMERGE and ADNI-1) as compared to the existing approach of [PERSON] et al. [20]. We have also validated the performance of [PERSON] et al.'s methodology on the updated ADNI-1 for a fair comparison. Our proposed methodology has outperformed [PERSON] et al.'s. All the results are cross-validated with 5-fold cross-validation. The accuracy of the proposed method for cognitive features of the ADNIMERGE dataset is not the best of all; however, these features are independent of any Magnetic Resonance Imaging (MRI) or neuroimaging tests, and we are interested in the early detection of dementia based on some cognitive tests. The ADNI-1 dataset contains a limited number of records, which creates overfitting. On the other hand, ADNIMERGE has many records that resolve the overfitting problem. Moreover, researchers have identified different features such as CDRSB, AV45-PET, FDG-PET, and ADAS-Cog13 that are important for the progression of AD [31; 32]. In contrast, our proposed methods have individually identified 4 neuroimaging and 18 cognitive features as more important for the detection of AD.  
When comparing our approach against existing models, particularly the methodology by [PERSON] et al., it is crucial to underscore the diversified feature set we utilized. While [PERSON] et al. predominantly relied on biomarker-derived features, our model uniquely integrated both cognitive and neuroimaging features, leading to better performance across both the updated ADNI-1 and ADNIMERGE datasets. Moreover, our approach showed greater flexibility; it  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & **Method** & **Dataset** & **Selected** & **Number of** & **Accuracy** & **AUC** \\  & & **Features** & **Records** & **zni(\%)** & **AUC** \\ \hline \multirow{4}{*}{Neuroimaging} & [PERSON] et al. [20] & ADNI-1 & 41 & 600 & 75.70 & 0.77 \\  & [PERSON] et al. [20] & ADNI-1 (Updated) & 41 & 5013 & 73.28 & 0.74 \\  & NCA-F & ADNI-1 (Updated) & 5 & 5013 & 79.4 & 0.88 \\  & NCA-F & ADNIIMERGE & 4 & 13,892 & 74.33 & 0.79 \\ \hline \multirow{4}{*}{Cognitive} & [PERSON] et al. [20] & ADNI-1 & 4 & 600 & 81.80 & 0.79 \\  & [PERSON] et al. [20] & ADNI-1 (Updated) & 4 & 5013 & 79.42 & 0.81 \\  & NCA-F & ADNI-1 (Updated) & 12 & 5013 & 87.89 & 0.95 \\  & NCA-F & ADNIIMERGE & 18 & 13,892 & 83.15 & 0.90 \\ \hline \multirow{4}{*}{Combined (Neuroimaging + Cognitive)} & [PERSON] et al. [20] & ADNI-1 & 41 + 4 & 600 & 84.90 & 0.86 \\  & [PERSON] et al. [20] & ADNI-1 (Updated) & 41 + 4 & 5013 & 82.17 & 0.84 \\ \cline{1-1}  & NCA-F & ADNI-1 (Updated) & 7 + 9 & 5013 & 87.39 & 0.95 \\ \cline{1-1}  & NCA-F & ADNIIMERGE & 7 + 12 & 13,892 & 83.42 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance Comparison of the Proposed Approach against the existing approaches.  
can adapt to resource-limited settings by using only cognitive features, thereby serving a broader clinical spectrum. Furthermore, the higher performance of our model substantiates the benefit of our proposed Neighborhood Component Analysis and Correlation-Based Filtration (NCA-F) in feature selection, which was pivotal in identifying the 4 neuroimaging and 18 cognitive features as most critical for AD detection.