# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Ensembles Learning in AD  
Combining multiple models--known as ensemble learning--often improves speed and performance. Multiple model predictions can be combined either through averaging or through more complex methods such as stacking or boosting. Ensemble strategies have evolved to improve the generalization of learning models, including techniques like bagging, boosting, stacking, and negative correlation learning (NCL) [64], while RF employs bagging to prevent overfitting by picking random feature subsets, bagging improves per  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Model Used** & **Limitations** & **Advantages** \\ \hline
[10,14; 15; 16; 26; 27; 30; 50] & CNN & Insufficient Explanation & Achieved high accuracy in extracting key features from MRI. \\ \hline
[14; 25; 45; 50] & SVM & Insufficient Explanation & Demonstrated strong performance in handling \\  & & & high-dimensional data with smaller sample sizes. \\ \hline
[5] & RNNs & Insufficient Explanation & Effectively captured temporal dependencies, improving \\  & & & prediction reliability. \\ \hline
[16; 28] & ResNet50 & Insufficient Explanation & Allowed deeper architecture while avoiding vanishing \\  & & & gradient issues. \\ \hline
[16] & Xception & Insufficient Explanation & Efficiently captured complex spatial features from MRI data. \\ \hline
[16] & SENet50 & Insufficient Explanation & Enhanced the detection accuracy through channel-wise \\  & & & attention mechanisms in the articles. \\ \hline
[24; 25; 37; 45] & RF & Insufficient Explanation & Robust against overfitting. \\ \hline
[10; 24; 25; 37] & DT & Insufficient Explanation & Capable of capturing non-linear relationships between features \\  & & & relevant to Alzheimer’s progression. \\ \hline
[10; 14; 25] & KNN & Insufficient Explanation & Adaptability in both classification and regression tasks. \\ \hline
[45; 50; 63] & XGB & Insufficient Explanation & Showed superior predictive performance through boosting. \\ \hline
[13; 16; 28] & VGG16 & Insufficient Explanation & Provided consistent feature extraction for hierarchical image \\  & & & structures in MRI analysis. \\ \hline
[25; 37] & AdaBoost & Insufficient Explanation & Improved prediction accuracy by combining \\  & & & multiple classifiers. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of Previous Articles.  
formance by producing multiple predictors from independent samples. By concentrating on incorrectly identified data, boosting techniques like AdaBoost and Gradient Boosting transform weak learners into strong ones. With the help of a meta-learning strategy, stacking combines several models to get better predictions. By reducing empirical risk, NCL encourages variation across ensemble models, while explicit/implicit ensembles seek to emulate ensemble behavior without appreciably raising computational costs. As described in the article [45] DPE can integrate different neural network architectures, such as CNNs and attention-based models, to take advantage of their complementary strengths. The study showed that the ensemble approach can achieve superior diagnostic accuracy compared to individual models, as it averages out the biases and variances of individual predictors. The study [65] presents a multi-task learning algorithm to predict AD progression using a similarity measure-based multi-task learning (MTL) approach to the spatiotemporal variation of brain biomarkers to model AD progression.