Harnessing Explainable Artificial Intelligence (XAI) based SHAPLEY Values and Ensemble Techniques for Accurate Alzheimer's Disease Diagnosis
Harnessing Explainable Artificial Intelligence (XAI) based SHAPLEY Values and Ensemble Techniques for Accurate Alzheimer's Disease Diagnosis
Bala Krishnan Raghupathy,Manyam Rajasekhar Reddy,3 Authors,Manikandan Ganesan
TLDR
This paper investigates boosting ensemble ML schemes, such as XGBoost, LightGBM, and Gradient Boosting (GB), for AD diagnosis and SHAP for feature selection and achieves efficient results, with an accuracy of more than 94% with minimum features for the detection process.
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.