Alzheimer's Disease Prediction using Advanced Predictive Intelligence Model

Alzheimer's disease (AD), a neurodegenerative disorder that progresses over time, disrupts cognitive functions, ultimately resulting in severe dementia. The pathological hallmark of Alzheimer's disease is predominantly characterized by the presence of amyloid plaques and neurofibrillary tangles, with biomarkers essential for diagnosis. Traditional biomarkers, such as MRI, PET scans, and CSF measures, though effective, often detect the disease only at later stages and face challenges of high cost and limited accessibility. This study explores the potential of machine learning (ML) and deep learning (DL) methods in predicting early-stage Alzheimer's disease (AD) by analyzing intricate datasets, such as genetic information, cerebrospinal fluid (CSF) biomarkers, neuroimaging data, and electronic health records (EHRs). The proposed model integrates various data types to enhance prediction accuracy and timeliness, allowing for early intervention strategies. With the rising prevalence of AD and its significant impact on individuals and society, innovative approaches leveraging ML and DL can improve diagnostic processes and patient outcomes. This research highlights the integration of advanced computational methods with conventional biomarkers, offering a comprehensive approach to AD prediction and management.