Alzheimer’s Disease Detection from Speech Using Shapley Additive Explanations for Feature Selection and Enhanced Interpretability

Smart cities provide an ideal framework for the integration of advanced healthcare applications, such as early Alzheimer’s Disease (AD) detection that is essential to facilitate timely interventions and slow its progression. In this context, speech analysis, combined with Artificial Intelligence (AI) techniques, has emerged as a promising approach for the automatic detection of AD, as vocal biomarkers can provide valuable indicators of cognitive decline. The proposed approach focuses on two key goals: minimizing computational overhead while maintaining high accuracy, and improving model interpretability for clinical usability. To enhance efficiency, the framework incorporates a data quality method that removes unreliable speech segments based on duration thresholds and applies Shapley Additive Explanations (SHAP) to select the most influential acoustic features. SHAP is also used to improve interpretability by providing global and local explanations of model decisions. The final model, that is based on Extreme Gradient Boosting (XGBoost), achieves an F1-Score of 0.7692 on the ADReSS dataset, showing good performance and a satisfactory level of clinical utility. This work highlights the potential of explainable AI to bridge machine learning techniques with clinically meaningful insights in the domain of AD detection from speech.