Towards Early Detection of Alzheimer’s Disease based on Speech using Reinforcement Learning Feature Selection

Alzheimer’s Disease (AD) is a progressive, irreversible neurodegenerative disorder and the leading cause of dementia worldwide, affecting an estimated 55 million people globally. The window of opportunity for intervention is demonstrably narrow, making reliable early-stage detection a clinical and scientific imperative. While current diagnostic techniques such as neuroimaging and cerebrospinal fluid (CSF) biomarkers carry well-defined limitations in scalability, cost, and access equity, speech has emerged as a compelling non-invasive proxy for cognitive function evaluation.

This work presents a novel approach for using acoustic feature selection as a decision-making technique and implements it using deep reinforcement learning. Specifically, we use a Deep-Q-Network (DQN) agent to navigate a high dimensional feature space of over 6,000 acoustic features extracted using the openSMILE toolkit, dynamically constructing maximally discriminative and non-redundant features subsets. In order to capture the latent structural dependencies among

acoustic features which classifier and wrapper methods have difficulty to model, we introduce the Graph Convolutional Network (GCN) based correlation awareness feature representation layer that operates as an auxiliary input to the DQN state encoder. Post selection interpretability is reinforced through TF-IDF weighting and K-means clustering which together yield both feature level and cluster level explanations that are clinically actionable. The framework is evaluated across five classifiers, namely, support vector machines (SVM), logistic regression, XGBoost, random forest, and feedforward neural network. We use 10-fold stratified cross-validation on established benchmarks of datasets, including DementiaBank Pitt Corpus, Ivanova, and ADReSS challenge data. The proposed approach is benchmarked against state-of-the-art feature selection methods such as LASSO, Recursive feature selection, and mutual information selectors. This research contributes to three primary intellectual advances: (1) a graph augmented state representation that encodes inter-feature relational structure within a reinforcement learning agent, (2) a clinically interpretable pipeline that bridges the gap between algorithmic performance and translational utility, and (3) multilingual data approach for the reinforcement learning agent framework. This study has direct implications for equitable, low-cost and scalable AD screening in both clinical and community settings.