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
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 im
Advances in machine learning and data availability, specifically the ADNI dataset, are enabling more sophisticated early detection methods for neurological diseases.
Early and accurate detection of Alzheimer's allows for timely intervention, potentially slowing disease progression and improving patient outcomes, while also reducing the societal and economic burden.
The ability to accurately classify cognitive states earlier using routine clinical data could transform diagnostic pathways and drug development for neurodegenerative diseases.
- · AI in healthcare companies
- · Pharmaceutical companies
- · Patients with neurological conditions
- · Clinical diagnostics sector
- · Traditional diagnostic methods
- · Late-stage pharmaceutical interventions
Improved early diagnosis of Alzheimer's disease using explainable AI models.
Accelerated development of targeted therapies and preventative measures as patient cohorts for trials become more precisely defined.
Potential for AI-driven disease prediction to become a standard component of preventative healthcare, integrating into routine medical check-ups.
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Read at arXiv cs.LG