An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
arXiv:2603.04449v1 Announce Type: new Abstract: Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-sco
arXiv:2603.04449v1 Announce Type: new Abstract: Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques, including SHAP and feature importance analysis, highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants. The results demonstrate that the proposed framework provides a reliable and transparent approach to Alzheimer's disease prediction, offering strong potential for clinical decision support applications.
Executive Summary
This research proposes an explainable ensemble learning framework for Alzheimer's disease prediction. The framework incorporates rigorous preprocessing, advanced feature engineering, and optimized modeling using multiple ensemble algorithms and a deep artificial neural network. The results demonstrate superior performance of ensemble methods over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants. The framework provides a reliable and transparent approach to Alzheimer's disease prediction, offering strong potential for clinical decision support applications.
Key Points
- ▸ The proposed framework incorporates rigorous preprocessing and advanced feature engineering techniques.
- ▸ Ensemble methods outperformed deep learning in Alzheimer's disease prediction.
- ▸ XGBoost, Random Forest, and Soft Voting showed the strongest accuracy, sensitivity, and F1-score profiles.
- ▸ Explainability techniques highlighted MMSE, Functional Assessment Age, and engineered interaction features as key determinants.
- ▸ The framework has strong potential for clinical decision support applications.
Merits
Strength in Explainability
The incorporation of SHAP and feature importance analysis provides a transparent and interpretable approach to Alzheimer's disease prediction, which is essential for clinical decision-making.
Robust Modeling Techniques
The use of multiple ensemble algorithms and a deep artificial neural network ensures robust modeling and generalizability of the framework.
Clinical Relevance
The framework's focus on structured clinical, lifestyle, metabolic, and lifestyle features makes it relevant and applicable to real-world clinical settings.
Demerits
Limited Generalizability
The framework's performance may not generalize to other datasets or populations, which is a common limitation of machine learning models.
Dependence on High-Quality Data
The framework's accuracy and reliability are heavily dependent on the quality and completeness of the input data, which can be a challenge in real-world clinical settings.
Expert Commentary
This research is a significant contribution to the field of Alzheimer's disease prediction and diagnosis. The incorporation of explainability techniques and ensemble methods provides a robust and transparent approach to modeling complex clinical data. However, the framework's limitations, such as its dependence on high-quality data and limited generalizability, must be acknowledged and addressed in future research. The results have significant practical implications for clinical decision-making and the development of new diagnostic tools and treatments. Furthermore, the framework's emphasis on explainability and transparency has important policy implications for the use of artificial intelligence in healthcare.
Recommendations
- ✓ Future research should focus on addressing the framework's limitations, such as its dependence on high-quality data and limited generalizability.
- ✓ The framework's results should be replicated and validated in other datasets and populations to ensure generalizability and robustness.
- ✓ The framework's emphasis on explainability and transparency should be incorporated into healthcare policy and regulation to ensure responsible and transparent use of artificial intelligence in medical diagnosis and treatment.