Predictive Precision Harnessing AI for Early Alzheimer's Detection

Abstract

The early detection of Alzheimer's disease (AD) remains a critical challenge in neurology and geriatrics, with significant implications for patient outcomes and healthcare systems. Recent advancements in artificial intelligence (AI) offer promising avenues for enhancing predictive precision in identifying early-stage AD through biomarker analysis. This paper explores the integration of AI methodologies with biomarker data to improve early detection rates of Alzheimer's disease. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes cerebrospinal fluid (CSF) biomarkers, neuroimaging data, and clinical assessments, we evaluate the performance of two AI algorithms: the Random Forest Classifier (RFC) and a novel deep learning model named NeuroCognitionNet (NCN). The RFC achieved an accuracy of 87%, sensitivity of 85%, and specificity of 89%, . In contrast, NCN achieved superior results with an accuracy of 92%, sensitivity of 90%, and specificity of 94%, highlighting its ability to effectively synthesize and interpret complex biomarker data.