# Alzheimer's Disease Diagnosis Using Machine Learning: A Survey
## 5 Discussion  
The determination of the optimal combination of various indicators is of utmost importance. One of the primary focal points in multi-modality studies revolves around establishing a methodology that can efficiently incorporate information obtained from different modalities. Using \"feature concatenation,\" in which the extracted features from all of the inputs are combined into a single vector, is an excellent way to do things. But combining data without considering that similar disease patterns can show up in the same part of the brain across all modalities can lead to an inaccurate detection model. Moreover, including supplementary variables, such as genetic data, poses a significant challenge when attempting to ascertain the various factors involved in Alzheimer's disease detection. The resolution of incomplete datasets is of utmost importance in studies involving multiple modalities. One significant obstacle faced in studies involving various modalities is the inherent incompleteness of the data, where specific modalities may be missing for certain subjects. This matter suggests that utilizing a singular deep model to encompass all modalities is contingent upon the availability of comprehensive multi-modality data possessed by subjects. It is worth noting that such data account for a significant portion of all multi-modality studies. Nevertheless, they limit the extent of the model. One potential approach to tackle this issue entails the creation of a three-stage framework for deep feature extraction and fusion in the context of magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data. Further investigation is required to examine the process of data generation. Even though different strategies, such as transfer learning and data augmentation, have been used to prevent overfitting, the lack of enough data samples is still a significant problem that makes it hard to generalize. Generative models can mitigate overfitting, which refers to excessively tailoring a model to the training data. In this context, data generation entails the creation of novel images based on existing ideas, thereby augmenting the dataset. As previously stated, the correlation between magnetic resonance imaging (MRI) and positron emission tomography (PET) can be mathematically represented to estimate the absence of PET scans based on the presence of MRI scans. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Reference** & **Methodology** & **Performance (\%)** \\ \hline
[116] & SVM & 92.48 \\
[117] & Fisher & 96.32 \\
[118] & SVM with genetic algorithm & 96.80 \\
[118] & SVM with image processing & 93.30 \\
[119] & Multi-modal neuroimaging & 91.40 \\
[120] & Image processing and weight extraction operations & 95 \\
[123] & Stochastic random forest & 93 \\
[124] & PCA with SVM & 95 \\
[129; 131; 132] & CNN with various architectures (average) & 98.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Most existing methodologies for the detection of Alzheimer’s disease. There is a significant demand for conscious explanations of intricate, deep architectures. Convolutional neural networks (CNNs) are extensively employed in deep learning architectures; however, selecting and designing an appropriate CNN model for Alzheimer's disease (AD) detection lacks a definitive methodology. Determining the quantity and arrangement of convolutional and fully connected layers is typically made through a subjective decision-making process based on arbitrary choices or informed by previous experience. Several CNN models are currently being utilized; however, the researchers have not comprehensively explained their methodology for selecting these models. The selection of the dataset is crucial and has the potential to impact the outcome of the classifier. The presence of diverse datasets, varying subject quantities, and distinct subject number codes often renders comparing different methods unfeasible.