Leveraging Explainable AI for Dementia Classification: A Machine Learning Approach

Topic: Risk, prevention and care in population studies

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.

Key findings

• 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.

Why it matters

This study compared several machine learning models for classifying dementia and used explainability techniques to interpret their predictions, with Random Forest performing best. It is a classification study and does not treat, cure, or prevent Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.37394/232029.2025.4.3
• Original source →
• Free full text: International Journal of Applied Sciences & Development →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{kumar2025-50f19073,
  author  = {Kumar, R Ram and Manoj, T and Shetty, Sucharitha and Prabhu, Omkar},
  title   = {{Leveraging Explainable AI for Dementia Classification: A Machine Learning Approach}},
  journal = {International Journal of Applied Sciences \& Development},
  year    = {2025},
  doi     = {10.37394/232029.2025.4.3},
  url     = {https://doi.org/10.37394/232029.2025.4.3}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Prevalence of dementia in India: National and state estimates from a nationwide study Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023-01-13 · usefulness score 76/100 · Disparities
• Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients JAMA network open · 2021-12-01 · usefulness score 66/100 · Digital health tools
• A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features Frontiers in neuroinformatics · 2020-06-11 · usefulness score 56/100 · Brain scans and imaging
• Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease Translational research : the journal of laboratory and clinical medicine · 2018-01-10 · usefulness score 51/100 · Digital health tools
• Predicting Cognitive Impairment and Dementia: A Machine Learning Approach Journal of Alzheimer's disease : JAD · 2020-01-01 · usefulness score 49/100 · Digital health tools

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• A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia Neural networks : the official journal of the International Neural Network Society · 2019-12-14 · usefulness score 47/100
• Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024-06-25 · usefulness score 57/100
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• Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain BMC Nursing · 2025-01-21 · usefulness score 55/100

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• Effect of public deliberation on attitudes toward surrogate consent for dementia research Neurology · 2011-10-05 · usefulness score 61/100 · Treatments
• Behaviour in frontotemporal dementia, Alzheimer's disease and vascular dementia Acta neurologica Scandinavica · 2001-06-01 · usefulness score 36/100 · Clinical safety
• Mapping the Numbers of Dementia in Brazil: A Delphi Consensus Study International Journal of Geriatric Psychiatry · 2025-02-01 · usefulness score 43/100 · Risk factors

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