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)…

Researchers developed an XGBoost machine learning model using eight routine clinical features from the Alzheimer's Disease Neuroimaging Initiative dataset that achieved 94.3% accuracy and a 0.982 macro AUC in classifying normal cognition, mild cognitive impairment, and Alzheimer's disease across 1,888 subjects. The model's SHAP analysis identified CDR Global as the dominant predictor for normal cognition and mild cognitive impairment, while CDR Sum of Boxes and MMSE scores drove Alzheimer's classification, providing clinically interpretable results that could enable earlier detection of the disease affecting over 55 million people worldwide.

arXiv:2606.03995v1 Announce Type: new Abstract: Background: Alzheimer's disease AD affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition NC , mild cognitive impairment MCI , and AD from routine clinical assessments remains a critical unmet need. Methods: An XGBoost classifier was developed for three-class detection using eight clinical features from the Alzheimer's Disease Neuroimaging Initiative ADNI : MMSE, CDR Global, CDR Sum of Boxes CDR-SB , MoCA, FAQ, age, sex, and education. Hyperparameters were optimised using Optuna 50 trials ; class imbalance was addressed with SMOTE. Performance was evaluated by macro AUC-ROC with 1,000-iteration bootstrap 95% confidence intervals, macro F1, balanced accuracy, and Cohen's kappa. SHAP values provided feature-level explainability. Results: The dataset comprised 1,641 baseline subjects 608 NC, 767 MCI, 266 AD . On five-fold cross-validation, mean macro AUC was 0.983 SD 0.007 , accuracy 0.944 SD 0.006 , and macro F1 0.929 SD 0.008 . On the held-out test set n = 247 , macro AUC was 0.982 95% CI: 0.965--0.995 , accuracy 0.943, balanced accuracy 0.932, macro F1 0.927, and Cohen's kappa 0.909. SHAP analysis identified CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drove AD classification. Conclusion: An explainable machine learning model trained on routine clinical assessments achieves near-perfect three-class Alzheimer's detection. SHAP analysis reveals clinically plausible, class-specific feature importance patterns supporting clinical validity. Future work will extend this framework with speech biomarkers for multimodal detection.