Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction: a SHAP-based Approach

Raja Farrukh Ali, Ayesha Farooq, John Woods, Emmanuel Adeniji, Vinny Sun, William Hsu

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Keywords: reinforcement learning

TL;DR: How SHAP can be used to explain RL models that predict disease progression

Abstract: In this study, we present a novel application of SHAP (SHapley Additive exPlanations) to enhance the interpretability of Reinforcement Learning (RL) models for Alzheimer's Disease (AD) progression prediction. Leveraging RL's predictive capabilities on a subset of the ADNI dataset, we employ SHAP to explain the model's decision-making process. Our approach provides detailed insights into the key factors influencing AD progression predictions, offering both global and individual, patient-level interpretability. By bridging the gap between predictive power and transparency, our work empowers clinicians and researchers to gain a deeper understanding of AD progression and facilitates more informed decision-making in AD-related research and patient care.

Submission Type: Long Paper

Submission Number: 7