Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

The explainability scope is the extent of explanations generated by the XAI method. It can interpret either an entire model or a specific instance of the model based on input test data. Accordingly, the explainability of a model can be either local or global. A global method explains the whole model by considering the entire inferential data set of the model. It gives a general perspective of the relationship of the model with all input instances. The popular DT algorithm can be intrinsically global in nature because the decision-making for all input data instances can be easily explained by visualising in a tree form.

On the other hand, the goal of a local method is to explain only a few instances of test data to the user. Local explanations help understand why certain decisions were made and can increase user confidence in specific examples. In the case of DTs, a local explanation can correspond to a single branch in the tree. It is worth noticing that, combining local inferences made through different input instances can yield global insights for the model. Pictorial representation of local vs global explanation can be represented as shown in Fig. 5.

An XAI method can generate explanations for a model either during or after the training of the model (see Fig. 6). Based on these two ways, XAI methods are further classified as Ante-hoc and Post-hoc [98]. Ante-hoc is a Latin word that literally translates into before-this. Hence, the goal of ante-hoc XAI methods is to provide explainability before the beginning of model training. Such Ante-hoc methods are transparent and self-explanatory and make the model explainable naturally while maintaining optimal accuracy [98]. ML algorithms that are ante-hoc in nature are linear regression, DT, and Bayesian models. These models are also referred to as white box or glass box models.

The Latin word Post-hoc translates to after-this. Such methods aim to provide explainability after a model has been trained. An external explainable model, called a surrogate model, is augmented to provide explanations for a trained blackbox model. Generally, support vector machines (SVM), and CNN are the ones where the inference mechanisms remain completely unknown to users that necessitate post-hoc models. Gradient-weighted Class Activation Mapping (GradCAM) [137], Layer-wise Relevance Propagation (LRP) [138] and Local Interpretable Model Agnostic Explanations (LIME) [100] are some common examples of XAI frameworks that can be applied on surrogate models for generating explanations.

Applicability of models refers to a concept of XAI where explainable methods are restricted to particular models or applied to any model as a post-process. The former is called model-specific, and the latter is a post-hoc approach called model-agnostic. Model-specific is an intrinsic approach where explainability is integrated into the model architecture and is not transferable to any other model architecture. For instance, interpreting a neural network model’s weight or activation values is specific to that neural network learning approach. Model-specific approaches for deep neural networks work by traversing the path of CNN in reverse order highlighting specific regions of the input image that majorly contributed to the decision. The guided backpropagation [139] and LRP [138] are example model-specific approaches.

On the other hand, model agnostic methods do not consider any of the internal components like weight or activation values and can be used with any learning approach. They extract explanations by causing perturbation and mutation to the input data and subsequently observing the sensitivity of the performance compared to the original data. In other words, by perturbing the input or weights of important features we can measure how much it has influenced the model performance. This will provide valuable insights into a localised region of input data that underwent perturbation. Alternative methods used are Occlusion Sensitivy analysis [134], GradCAM [137] and Feature Importance [126]. Some popular model-agnostic approaches are LIME [100] and SHapley Additive exPlanations (SHAP) [140]. Figure 7 depicts a few key differences between these two types of XAI methods.

The classification model for images can differ substantially from those that classify using text, categorical, or temporal data such as speech. Therefore, the input formats (numerical, visual, textual or temporal) for a model can play a vital role in framing different forms of explanations for XAI methods. The interpretations of predictions can be in many forms and depend on the end users’ needs and concerns. There are four forms of explanations commonly used to interpret a prediction: numerical, visual, rule-based, and textual [98].

The numerical explanations generated by models are usually a measure of the input variables that contribute to the model’s outcome. They represent numerical formats like values, vectors of numbers, or matrices. A numerical explanation can also be a probability measure assigned to a neural network layer. Visual explanations are the most common way to explain the functioning of a model in a graphical way. For example, heatmaps can highlight important areas of an image that are influential for the decision. A visual explanation can be easily understood by novice end users of the AI model. Textual explanations are precise and specific and generally used for individual predictions. It is not a commonly used form of explanation due to its high computational complexity requiring natural language processing (NLP). However, they are easily understandable to humans and are mostly generated for local scope. Rule-based explanations are simple and basic forms of explanation which are more structured than visual and textual explanations. They are intuitive to humans and can be used to explain the prediction of models using IF-THEN rules or trees with AND/OR operators [98]. This type of explanation is mainly utilised for ante-hoc methods and interprets models with global scope. A detailed discussion of these forms of explanations is dealt with in "XAI Frameworks for AD Detection" and "Benefits of using XAI Methods for AD Detection" in the context of AD.

LIME is an open-source tool used to generate explanations for a single instance instead of the entire dataset, hence the term local. LIME provides explanations by perturbing the model’s input data, creating a surrogate model, and observing the changes in prediction and selecting the top significant features [100]. Due to agnosticity of the LIME model, it is used after the model has been trained for prediction and can be used for any blackbox model. For blackbox explanations, LIME can interpret image classifications, explain text-based models and tabular datasets in either textual, numeric or visual form (for more details, see section XAI Frameworks for AD Detection).

SHAP is an XAI technique that assigns a weight, called Shapley value, to each feature of a trained model [140]. These features with an assigned weight are observed for all possible weighted input combinations. The contribution of each of the Shapley value-added features, for all possible weighed input combinations, is observed based on its efficiency, symmetry, features with no zero contributions and cumulative contribution of a feature with sub-parts. SHAP shows performance consistency and provides good accuracy for predictions in the local scope. In this review, SHAP was commonly used in conjunction with numerical data to provide a visual analysis of blackbox models (see Figs. 15, 16, and 26).

GradCAM is a technique used to make CNN models more transparent by identifying the important regions of an input image for predictions [146]. GradCAM is applied using gradient information of the output layer of the CNN model to produce a localisation map representing crucial regions in an image. This is achieved by assigning important value for each neuron for making specific decisions. Therefore, the final output of GradCAM is a coarse heatmap that highlights important regions suitable for prediction and explanation (see Fig. 19).

LRP is another tool like GradCAM that generates a heatmap with highlighted regions of an image [138]. LRP is used in CNN where inputs can be images or videos. LRP assigns relevance scores to all neurons of a specific output for the last layer of a CNN. LRP then propagates in reverse until the input layer by computing scores for every activation unit (neuron) in each layer. Using the final relevance score, a heat map is generated by LRP as an explanation that can be used to identify influential regions in the prediction (see Figs. 17 and 18).

ICE is an extension of Partial Dependence Plot (PDP) that is used to produce visual explanations for blackbox models [145]. PDP is a visualisation obtained by plotting the average predicted outcome of a model by varying the value of one feature of interest and keeping the other feature values constant. ICE disaggregates the PDP by creating individual plots for each instance of blackbox model predictions by altering the value of a feature of interest and leaving other feature values unchanged [126]. The outcome can be visualised as a line plot which is a set of points for an instance with the altered feature value and the respective predictions (see Fig. 20).

In an image predicting AD, it is necessary to explain or identify areas in the image that contribute to AD classification. OSA is a technique initially proposed by Zeiler and Fergus [144]. In this technique, portions of the input image are occluded or hidden with a grey or black patch, creating a heatmap. The variations in the output probability of the occluded image are observed [147]. The most critical region, if occluded, will have the highest impact with low probability. An occlusion sensitivity map is therefore used to locate important patches of the image responsible for AD.

The SM is another important concept of deep learning, which was first introduced by Simonyan et al. [148]. Unlike an occlusion map where portions of the input image are hidden with a black patch and creating a heatmap, in SM each pixel of an AD-classified image is removed and subsequently processed. The heatmap obtained is checked for probability variations where a low probability indicates that the removed pixel plays a vital role in AD classification [149]. Therefore the output heatmap that undergoes the Saliency technique has all important pixels in the image eligible for explaining the disease.

Table 2 provides XAI framework categorisation based on scope, applicability, implementation and interpretable forms for some popular XAI tools used in the literature. The table also provides links to GitHub repositories for rapid reproducibility.

El-Sappagh et al. [122] have proposed a multimodal prediction and detection of AD in two stages. In the first stage, the model performs a multi-class classification for the early diagnosis of AD. A binary classification model will follow in the second stage to detect possible MCI to AD progression. The authors have utilised 11 different modalities: PET, MRI, cognitive scores, genetic data, demographic, and other clinical data. The classification was done using the RF algorithm resulting in an overall F1-score of 93.94% and 87.09% in two classification stages. The authors have used explainers based on the DT and fuzzy rule, providing complementary justifications for every prediction made in each stage.

Authors in [125] provided local and global interpretation in the conversion of Dementia to MCI using the XGBoost algorithm. They have utilised multimodal data (personal information, gene expression, PET and MRI measures, cognitive score) in the four-way classification of stages of AD progression. The model achieved an accuracy of 84.0%. The interpretation methods provided insights about data modality influential in each stage of AD progression.

Yang et al. [150] provided visual explanations for the deep 3D CNN in AD classification. The authors have utilised the brain MRI scans from the ADNI dataset for AD vs. HC classification using ResNet and VGG16 architectures. The authors also proposed a variant of ResNet architecture called ResNetGAP, where the Global Average Pooling(GAP) layer was introduced in the original ResNet architecture instead of the conventional Maxpool layer. The approach yielded an overall accuracy of 76.6% and an AUC of 0.863. Regarding interpretability, the authors could produce visual explanations for AD prediction using the network weight map from three different network architectures. The visual interpretation highlighted the cerebral cortex, lateral ventricle, and hippocampus regions in the 2D slices of the brain MRI.

In another study [133], authors used RF and XGBoost algorithms in the HC vs. AD classification using socio-demographic data, medical history, and lifestyle parameters (daily activity and smoking). The study developed an ensemble-based ML model to predict AD and explained the prediction in local and global contexts. The study also includes feature importance analysis and ranked the dominant features influential in AD. The top 7 features considered by both classifiers (RF and XGBoost) in AD prediction were the same. The feature importance analysis also found the least suspected risk factors driving the risk of AD.

Ilias and Askounis [131] have proposed transformer-based models for the identification of Dementia using voice transcripts as data modality. This is the only work found in the review that uses voice with associated text as data modality in Dementia identification and subsequent interpretation. The study involves the identification of Dementia in the first stage (HC vs. AD), followed by identifying the severity of Dementia in the second stage. The authors have employed Bidirectional Encoder Representations from Transformers (BERT). To distinguish between the languages used by AD patients and non-AD patients, word symbols were colour-coded for interpretation.

Another study [157] identifies MCI and AD patients (3-way classification HC vs. MCI vs. AD) using 90 seconds recording of resting state EEG. The study compares the performance of classification using three classifiers: SVM, ANN, and CNN. The explainable component in this study aimed to highlight the brain region most indicative of the onset or progression of MCI/AD.

Lombardi et al. [151] used multimodal data for AD vs. MCI vs. HC classification using the RF classifier. The clinical and neurophysiological indices were used to train the RF classifier in the AD classification. The authors explored various neurophysiological data’s capabilities in predicting different degrees of cognitive impairment. The dominant features used by the classifier for prediction have been enriched with explainable values.

Authors in [153] have used a metabolomic dataset to identify the key metabolites and their interaction associated with AD. The authors have used SVM and RF as classifiers. The model interpretation was provided by ranking significant metabolite features in the prediction based on the Gene importance. The authors claimed that the study provided explanations that could give additional background for the metabolomic backdrop of AD.

Rieke et al. [134] have used 3D CNN in the binary classification task (AD vs. HC) using structural MRI scans of the brain. The authors emphasised the importance of applying different visualisation methods for identifying various brain regions. For instance, a particular visualisation method could highlight the temporal lobe, whereas other techniques could focus on cortical areas. Such details obtained from different visualisation methods help find the distribution of relevant patterns which could vary across patients.

Bloch and Friedrich [161] used Shapley values to interpret XGBoost, SVM, and RF blackBox models using the ADNI dataset. The study considered MRI volume features, cognitive test results, sociodemographic data, and Apolipoprotein alleles. The Shapley values were employed to visualise the feature association in the blackbox classification. The models were trained individually using separate data modalities. The examination found a biological correlation and enhanced results when these models included cognitive test results.

Ruengchaijatuporn et al. [124] proposed a two-way classification for differentiating MCI vs. HC patients using the VGG16 and custom CNN architecture incorporating a self-attention mechanism (Conv-Att). The authors considered digital drawings (clock, cube, and trail making) collected from HC and MCI patients to train the models. The VGG16 model interpretability was provided using the GradCAM, and custom CNN has a built-in self-attention mechanism to offer visual cues. Clinical experts validated the visualisation produced by the GradCAM and the Conv-Att model using a simple rating mechanism. The authors concluded that the heatmap produced by the Conv-Att model was better aligned with the expert’s clinical experience. However, a serious limitation of this study is the non-consideration of a biomarker.

Another study [101] utilised the CNN architecture using 18F-FDG PET images to classify AD vs. MCI vs. HC. The model achieved AUC scores of 0.81, 0.63, and 0.77 for HC, MCI, and AD cases. For explanations, heatmaps were generated and registered with the Talairach atlas (3-dimensional coordinate system of the human brain), indicating each voxel’s importance for the final classification decision.

Table 8 provides a complete summary of XAI methods used in the blackbox interpretability for AD detection. The chart in Fig. 13 provides a clear perspective of research using different XAI methods. In particular, we find that studies conforming to the concepts of Local (Ll), Post-hoc (Ph), and Model-agnostic (Ma) make up the totality of the volume. In this context, most of the studies have been concentrating on classifiers under DL, of which a majority study deals with CNN. Furthermore, it is clear that a subsidiary part of the research focuses on Global, Post-hoc, and Model-agnostic, where classification techniques are widely used within the framework of ML approaches. Cumulatively, it can be understood that the Model agnostic approach covers 31 out of 37 studies considered in our review.

The field of dementia detection using transcripts with the transformer-based network - BERT by Ilias et al. [131] produces promising classification results. The authors illustrate how transcripts using LIME explain the classification between dementia and non-dementia patients. Figure 24 shows texts in different colours to identify between the labels AD and HC. The tokens or textual forms in transcripts are assigned different colours, indicating which tokens indicate a control group. The intensity of colours for the tokens indicates the importance of these markers for the final transcript classification.

In another study, Salih et al. [159] try to develop a proxy that will check for stability in the explanation by choosing the proper XAI method, classifier, and available data. The authors have used Principal Component Analysis (PCA) to verify the stability of the identified predictors with the chosen explainer by quantifying (in numeric form) the informative predictors. In this study, the measure associated with predictors using SHAP and the proxy PCA produces uncorrelated variables that give stable ranking for most classifiers. Figure 25 shows a correlation score of different models for identified features. Due to the widespread use of XAI in delicate fields, including the prognosis of long-term mortality, admission to critical care units, and extubation failure, the results are beneficial to the medical community.

We found two articles in our review that obtain explanations in the form of rules. One study integrates the Internet of Things (IoT) and AI agents to remotely monitor seniors’ health status. Khodabandehloo et al. [163] offer a novel HealthXAI system that employs a DT regression method to aid in the early identification of cognitive decline and give caregivers high-level numerical scores reporting inappropriate behaviours and explanations of the forecasts in natural language. The decision rule predicts the value of the target variable and interprets it with a natural language description as either HC or AD. The suggested strategy addresses the problem of ongoing remote monitoring of elderly individuals to aid in the early identification of cognitive decline and to better assist clinicians in reaching a diagnosis. In another study, García-Gutierrez et al. [162] proposed a diagnostic tool that uses a DT that provides a simple and unambiguous set of decision rules to provide capabilities to clinicians to give insights into the pathophysiology of AD and behavioural Fronto Temporal Dementia (bvFTD). This paper is beneficial for early detection and diagnosis in the medical field because it outlines all the processes needed to evaluate the datasets, including data preparation, selection of features using an evolutionary approach, and in the creation of a model for the disease discussed in the paper.

The data models in the studies that use LRP as an AI explainer include CNN and 3D CNN. LRP provides visual explanations as heat maps of significant areas of the brain in identifying brain atrophy. The studies’ significant features recognised for interpretation by LRP include the hippocampus, entorhinal cortex, and amygdala. Böhle et al. [121] discuss using LRP with guided backpropagation in discovering the heat maps with relevant significant features. Pohl et al. [132] state that they have discovered similar significant features by using composite LRP, using many propagation rules. The author also identifies that damage to the left and right temporal lobes causes problems with verbal semantic memory and visual memory, respectively. Both authors contribute these findings to the benefit of clinicians and radiologists in diagnosis and building trust in the system.

Several studies use the GradCAM XAI tool for a visual explanation of the predictions of a DL model. Ruengchaijatuporn et al. [124] use GradCAM to visually explain predictions from a VGG16 deep learning model. The DL model has three types of neuropsychological test inputs: clock score prediction, cube-copying drawing, and trail-making inputs. However, the authors prove the benefit of using a CNN model with self-attention work more efficiently than VGG16 with GradCAM. The heat maps proved beneficial to experts with clinical experience and are rated far superior to the baseline model. The authors also claim the model yields better classification performance and interpretability and benefits the domain community. In another study by Jain et al. [160], GradCAM was materialised to show heat maps of a four-way classification of AD predicted using a Generative Adversarial Network (GAN) model. Differently coloured heatmaps obtained from the system help inform predictions of the early onset and severity of dementia. The system has proved beneficial in accurately distinguishing between different classes and making appropriate early predictions. The research community benefits from the authors’ use of GAN to create a newly balanced dataset and their awareness of the serious issue with unbalanced datasets. The coloured heat map in the article, which showed the advanced characteristics of various stages of dementia, would aid medical professionals in making judgments. By proposing a 3D Residual Attention Deep Neural Network (3D ResAttNet) that is easy to understand, Zhang et al. [130] have developed an innovative computer-aided technique for the early diagnosis of AD. The authors assert that the 3D ResAttNet enhances the diagnostic performance and interpretability of MRI with GradCAM by capturing local, global, and spatial information. The study offers an entire end-to-end learning system for automated disease diagnosis. Furthermore, the suggested approach’s explainable process can identify and emphasise the role that crucial brain regions like the hippocampus, lateral ventricle, and most of the cortex play in transparent decision-making. Another study by Yang et al. [150] used different 3D-CNNs for classification and AI explainers, including 3D GradCAM. Experts in medicine can gain from the heat maps because they demonstrate how vital the lateral ventricle and most cortical regions are in classifying AD.

Most visualisation techniques consider only the last convolutional layer that extracts global features of pathological abnormalities but do not consider the small subjects and discrepancies. The research by Yu et al. [158] used the High-Resolution Activation Mapping (HAM) approach, which created high-resolution visual explanations that take into account values from the last convolutional layer and intermediate features. Compared to the previous efforts, high-quality heatmaps that display discriminative localisation of brain anomalies perform better. The authors validated the model’s effectiveness with good diagnostic accuracy and insightful explanations, which affirm fidelity in clinical applications.

Bordin et al. [165] created heatmaps using the occlusion sensitivity method by occluding a section of the input image with a black patch. The model’s brain regions contributing to the classification decision were easily discernible from fluctuations in the output probability predictions. The authors identified and reinforced the relevance of white matter hyperintensity as a neuroimaging biomarker for dementia. One of the studies used LRP to decompose the output score of the network with input 18 FDG PET images into individual contributions while maintaining the conservation principle and heat map produced. Using a Saliency map, the study also generates a voxel-wise heat map for each contribution. In their work, De Santi et al. [101] establish that the colour distribution, as opposed to LRP, emphasises a higher variation among the classes in the saliency map. This study demonstrates that the saliency map regarded the frontal-temporal space of the brain as a vital region for classifying all the classes. The occipital lobe, however, was the area that mattered most in LRP. In both studies, their finding proves significant clinical relevance and, in the long run, leads to increased trust and use of AI models.

The study proposed by Kim et al. [129] uses an interpretable Graph Neural Network (GNN) for classifying AD and MCI. Using GNNExplainer, the proposed model’s predictions are explained in light of the actual and predicted labels for HC, MCI, and AD. GNNExplainer visualises nodes of importance with a high region of interests representing a high contribution to the classification. The authors found that GNNExplainer gives encouraging interpretable results. Also, the explainer can capture the predictor’s neuro-anatomical contribution, giving more biological interpretations to better understand AD progression. The authors find the GNNExplainer beneficial as it outperforms other competing models (i.e., DNN, SVM) concerning prediction accuracy.

Several articles considered in the review have used LIME to visualise the explanations of AD predictions. Kamal et al. [127] have used LIME to discover the critical genes responsible for AD. The genes OR8B8 and ATP6V1G1 are found to be very important for AD by the authors. HTR1F and OR6B2 are therefore discovered to be important characteristics of HCs. The predictions about the likely outcome of the generated data are visualised using LIME by Shad et al. [128] and Sidulova et al. [157]. Coloured areas are used to denote the places that prompt models to classify images to make appropriate predictions.

The RF model has been used in numerous research to classify AD ([122, 123, 151, 154]) using SHAP to depict the explanations using force, summary and violin plots (see Figs. 15, 16 and 26 respectively). According to the study in [122], the output decision is supported by several complimentary, credible, and visible justifications. Additionally, the model displays a significant accuracy-interpretability tradeoff due to the accurate outcomes and great interpretability it produced. The proposed model is accurate and understandable, according to the authors. In [123], the author displays SHAP force plots that can explain specific model predictions widely used in clinical practice. The model displays the most significant features that are learned and show an acceptable relationship. The absolute value of each SHAP score reflects how much each attribute contributes to the final prognosis, as shown in [151] by the authors. The internal workings of the RF classifier that are trained with cognitive and clinical data are explained by SHAP, demonstrating a potential connection between feature relevancy patterns and diagnosis. In a different study [154], the authors demonstrate models with great prediction accuracy because they merge many DTs to create a single global forecast. Additionally, it repeated the study using the SHAP method and returned feature ranking results that agreed with those from RF. The study used AD biomarkers strong enough to predict HC, LMCI, and AD correctly and ranked biomarkers according to their significance. The paper also shows that the Amyloid beta (A), tau (T), and neurogenerative biomarkers(N) have different importance in predicting dementia. The study also establishes that the amyloid beta and tau status throughout disease progression plays a more significant role in predicting early cognitive impairment. The study also demonstrates that glucose consumption is more significant in predicting future cognitive impairment. The study incorporates biomarkers from all A, T, and N framework arms into a single integrated analysis, utilising RF to categorise dementia status and rank biomarker characteristics in order of relative importance.

XGBoost and RF are used in the study by Bogdanovic et al. [125] for AD classification and interpreted with SHAP. The classification model proves beneficial in obtaining exactness and validity in prediction results. The SHAP force plot in either model indicates that the feature clinical dementia rating scale has the highest impact. The features of gender and apolipoprotein, as seen from the SHAP force plot, have the most negligible impact and are not decisive factors for having an AD diagnosis [125]. On the other hand, the study also reveals that mini-mental state examination values impact mainly healthy subjects, and age influences the LMCI class. Danso et al. [133] also go through the benefits of the tree-based approach and how it can give details on decisions made concerning forecasts. The research created a machine-learning model with multiple classifiers to predict AD at both global and local levels. Traits such as education, hypertension, hearing loss, smoking, obesity, depression, physical inactivity, diabetes, and infrequent social interaction were highlighted as potential modifiable risk factors in the report and were among the best-ranked predictive model.

In their study, Hernandez et al. [155] compare XGBoost, RF, and SVM models to understand how to quantify each feature’s contribution and achieve the best accuracy. With the help of SHAP violin plots, the study identifies the best models that use information coherent with clinical knowledge. Figure 26 illustrates a violin plot that shows the important features based on the XGBoost classifier for the complete test samples. In Fig. 26 the feature values for various test samples are shown by a colour code, which helps to relate whether a specific feature value favours the high or low probabilities predicted by the model. Blue hues indicate low values, while red hues indicate high values on the colour scale. The author employs a similar justification to demonstrate the utility of the features in distinguishing between the classes.

Lai et al. [156] make use of learning models AdaBoost, LR, LGBM, DT, XGBoost, RF, kNN, Naive Bayes, and SVM along with SHAP to generate force plots to illustrate profiles of the afflicted patient and normal subjects. In this study, the authors found six genes that could accurately predict AD progression and used SHAP to explain the decision-making process of the model used. The study offers fresh perspectives on the function of ER stress-related genes in AD heterogeneity and the creation of brand-new immunotherapy targets for AD patients. The work of Chun et al. [126] uses learning models RF, SVM, and XGBoost. The study is significant because it shows that the Interpretable Machine Learning (IML) method can calculate the individual probability of dementia conversion for each MCI patient. This study’s fundamental discovery is that the IML, consisting of ICE, SHAP, and BreakDown plots, enabled the interpretation of variables crucial in each patient’s conversion to dementia. The authors affirm that a model using any IML techniques enables predicting patients’ conversion from amnestic MCI to dementia.

The study of Xu et al. [152] involves deep learning models that include Random Seeds and Nested Cross-validation, SVM-SMOTE, and RF for a three-way AD classification. Using SHAP, the paper identifies the feature RAVLT-perc-forgetting, and an explanation force-plot for every instance is obtained. The explanations of each instance of the test set can be rotated ninety degrees. Subsequently, the rotated instances are finally stacked horizontally, producing a SHAP summary plot [152]. They consequently provide the doctors with an understanding of how and why the model makes judgements. SHAP is used by Salih et al. [159] and Bloch et al. [161] to determine the order of informative predictors in test data. ML models and their relationships were also visualised and analysed using SHAP summary plots. SHAP force plots examined the individual forecasts of chosen individuals, and the summary plots of those models primarily displayed biologically conceivable outcomes. Moderate to significant correlations were found when comparing the relevance of natural and permutational features to SHAP values.

To summarise, the LIME explainer interprets transcripts predicted by BERT to predict textual tokens. SHAP was used to produce probabilistic prediction in numeric format, DTs produced rule-based ante-hoc interpretations, and other explainers like LRP and GradCAM supported the AD diagnosis by visualising heatmaps showing significant features. A total of 28 research articles out of 37 resorted to visual form representation, one article each for numeric and textual form, and the remaining two explained using the rule-based technique. This research question helped us bring to light the different forms of explanation for AD prediction and will be of significant use for future research.