Harnessing Explainable Artificial Intelligence (XAI) based SHAPLEY Values and Ensemble Techniques for Accurate Alzheimer's Disease Diagnosis
Received: 15 November 2024 | Revised: 27 December 2024 and 8 January 2025 | Accepted: 11 January 2025 | Online: 26 January 2025
Corresponding author: Manikandan Ganesan
Abstract
Machine Learning (ML) is a dynamic method for managing extensive datasets to uncover significant patterns and hidden insights. ML has revolutionized numerous industries, from healthcare to finance, and from entertainment to transportation. Ensemble classifiers combined with Explainable AI (XAI) have surfaced as a significant asset in the field of Alzheimer's Disease (AD) diagnosis. Boosting EC techniques coupled with Shapley Additive Explanations (SHAP) offers a powerful approach to AD diagnosis. This paper investigates boosting ensemble ML schemes, such as XGBoost, LightGBM, and Gradient Boosting (GB), for AD diagnosis and SHAP for feature selection. The proposed scheme achieved efficient results, with an accuracy of more than 94% with minimum features for the detection process.
Keywords:
machine learning, SHAP, ensemble classifiers, explainable AI, Alzheimer's diseaseDownloads
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Copyright (c) 2025 Bala Krishnan Raghupathy, Manyam Rajasekhar Reddy, Prasad Theeda, Elangovan Balasubramanian, Rajesh Kumar Namachivayam, Manikandan Ganesan
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