Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data

Résumé

Abstract Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression. We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.

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