TITLE:
Comparative Analysis of Anticholinergic Burden and Cognitive Decline in Elderly Patients on Long-Term Neuroleptic Therapy
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Geriatric Psychopharmacology, Anticholinergic Burden, Cognitive Decline, Interpretable Machine Learning, Neuroleptic Therapy, SHAP Explainability
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.6,
June
9,
2025
ABSTRACT: Elderly individuals undergoing long-term neuroleptic therapy are increasingly vulnerable to cognitive decline, a condition that significantly impairs quality of life and increases healthcare burden. One contributing factor is the cumulative anticholinergic burden from prescribed antipsychotic medications. This study aims to explore the relationship between anticholinergic load and cognitive impairment in aging patients using an interpretable machine learning framework. We developed a synthetic dataset of 1000 geriatric patient profiles with realistic distributions of clinical and demographic features, including age, comorbidity count, treatment duration, medication load, and baseline cognitive status. The binary target variable represented observed cognitive decline. Our approach involved robust preprocessing through KNN imputation, feature scaling, and one-hot encoding, followed by oversampling of the minority class using SMOTE. We trained and evaluated three predictive models—Random Forest, XGBoost, and Logistic Regression—using stratified cross-validation and hyperparameter tuning. Logistic regression outperformed the ensemble and tree-based models, achieving the highest ROC AUC of 0.702 on the test set. Feature importance analysis identified anticholinergic burden, age, and vascular disease as leading contributors to cognitive decline. Furthermore, SHAP (SHapley Additive exPlanations) values offered interpretable insights into individual prediction dynamics and global feature relevance. Logistic Regression outperformed XGBoost and Random Forest, achieving an ROC AUC improvement of +0.035 and +0.037 respectively, highlighting its superior discrimination capability in this setting. The findings validate the hypothesis that increased anticholinergic burden elevates cognitive risk and underscore the utility of transparent AI tools in medical decision-making. These results pave the way for integrating explainable machine learning into geriatric pharmacovigilance and cognitive health monitoring, with the potential to inform personalized treatment strategies and reduce adverse neurocognitive outcomes in vulnerable populations.