Feature Importance Analysis for Mini Mental Status Score Prediction in Alzheimer’s Disease

Howard Prioleau, Saurav Keshari Aryal

Keywords: Alzheimer's Disease, Mini Mental Status Exam, Machine Learning, Feature Importance, Automated Readability Index

TL;DR: Perform Modeling to Predict MMSE from Audio and Transcript Data. Followup with feature analysis of the most important feature.

Abstract: This research article proposes developing predictive models to forecast Mini-Mental State Exam (MMSE) scores using the 54 most important features identified from the current state-of-the-art model. The study employs the SHapley Additive exPlanations (SHAP) method to explore feature importance and interpret model performance. The analysis shows that the Automated Readability Index (ARI) is the most influential feature in predicting MMSE scores. This finding suggests that ARI's capability to capture language impairment and morphosyntax is valuable in predicting cognitive decline in dementia patients. Although the analysis could not evaluate all features, this study provides a foundation for future investigations into features that may assist in predicting MMSE scores and the onset of Dementia.