An Explainable Ensemble Machine Learning Framework for Early-Stage Alzheimer’s Disease Detection Using Clinical Data
DOI:
https://doi.org/10.46610/RRMLCC.2026.v05i01.004Keywords:
Alzheimer’s disease detection, Clinical data classification, Early-stage cognitive impairment, Ensemble machine learning, Explainable Artificial Intelligence (XAI), SHAPAbstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that is very important to cognitive functioning and quality of life, and therefore, early diagnosis is essential in effective management of the disease and its treatment. In this paper, an explainable ensemble machine learning model is proposed to detect early-stage Alzheimer 4 disease based on structured clinical information. They use a publicly accessible Alzheimer's clinical dataset, which is available on Kaggle, and it includes demographic features, cognitive assessment data (MMSE and CDR), as well as other clinical features. The suggested methodology is that the data is pre-processed98, features are selected, and several base classifiers such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost) are trained. These models are combined together in terms of a stacking-based ensemble approach to provide greater classification strength and predictive capability. Shapley Additive Explanations (SHAP) are added to provide transparency and clinical interpretability, both at the global and patient level of explanation. Experimental comparison shows that the ensemble structure is more accurate, precise, recalls, and F1-score, as well as ROC-AUC, than individual models, which are reliable and interpretable decision-support systems to identify early-stage Alzheimer disease. The proposed solution is a cost effective (scalable) and clinically significant solution to cognitive impairment screening.
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