A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?

“…The model outputs a sentence in natural language, explaining the involvement of attributes in the model's classification output. The model achieved a good performance while balancing the accuracy-interpretability trade-off in both AD classification and MCI-to-AD prediction tasks, allowing for actionable decisions that can enhance physician confidence, contributing to the realization of explainable AI (XAI) in healthcare [79].…”

“…Integrating multi-modal data improves the accuracy of predictions but increases model complexity, making them become non-interpretable (such models are known as black-box models) [112]. To address this issue, feature importance representation techniques, such as SHAP or Grad-CAM, have been developed to explain the decisional processes of black-box models [79,[113][114][115]. Second, AI can potentially integrate an infinite amount of data across different modalities, in order to increase the performance of prediction, thus improving their usefulness in clinical practice [116].…”

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

“…We have recognized this trend as more crucial for health care experts to overcome several challenges such as readiness of outcomes. Meanwhile, few numbers of studies intended to solve the black-box issue in the health care area [16,[30][31][32][33][34].…”

“…After that, the SHAP algorithm was applied to the prediction model for obtaining explanations of the features that drive patient-specific predictions to mitigate the issue of black-box predictions at any given time point. According to the study [33], the authors developed an accurate and interpretable Alzheimer's disease diagnosis and progression detection model. This model provided physicians with accurate decisions along with a set of explanations for every decision using 11 modalities of 1048 subjects from the Alzheimer's disease Neuroimaging Initiative real-world dataset.…”

The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

“…e authors in El-Sappagh et al [18] used ensemble machine learning classifiers based on RF for the two layers, utilizing multimodal AD datasets. Venugopalan et al [19] used different models including, SVM, DT, RF, and KNN, to early detect the AD stage.…”