Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

An XGBoost classifier was developed using clinical features from the ADNI dataset for multi-class detection of normal cognition, mild cognitive impairment, and Alzheimer's disease. The model achieved a high mean macro AUC of 0.983 and an accuracy of 0.944, with SHAP values providing feature explainability.