International Journal of Applied Sciences & Development
E-ISSN: 2945-0454
Volume 4, 2025
Leveraging Explainable AI for Dementia Classification: A Machine Learning Approach
Authors: , , ,
Abstract: In last two decades the growing life expectancy has spiralled the prevalence of neurogenerative disorder such as Dementia among elderly population posing a significant challenge to mental health globally. The dementia is generally characterized by progressive cognitive decline which it affects memory, thinking, behaviour, and the ability to perform everyday activities. Early diagnosis and intervention are crucial for improving patient outcomes and managing the societal burden of dementia. The primary aim of this study is to deploy various machine learning models such as Logistic Regression, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest (RF). Extreme Gradient Boosting (XGBoost) for the classification of Dementia. The suitable features required for the classification is determined through efficient filter-based and wrapper-based feature selection techniques. The experimental evaluation of machine learning models is performed using standard metrics such as accuracy, precision, recall and F1 score. Furthermore, Explainable AI (XAI) techniques such as SHAP and LIME are employed to interpret the black-box nature of these models, offering transparency and insights into the contribution of individual features to the model predictions. The thorough evaluation of machine learning models exhibited that Random Forest outperforms other models with an accuracy of 96%, with CDR identified as a key predictor through XAI analysis.
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Keywords: Dementia Classification, Explainable Artificial Intelligence, Health Informatics, Machine
Learning, Neuro-Disorder, Random Forest
Pages: 15-30
DOI: 10.37394/232029.2025.4.3