EARLY STAGE ALZHEIMER'S PREDICTION: INTEGRATING MACHINE LEARNING AND NEUROIMAGING BIOMARKERS

ARTIFICIAL INTELLIGENCE

Alzheimer's disease, an unrelenting and incapacitating neurodegenerative condition, presents an increasingly urgent global healthcare challenge. As the aging population continues to grow, the prevalence of Alzheimer's disease rises, emphasizing the crucial necessity for early detection and intervention. In this section, we offer a concise overview of the background, significance, and necessity for the current study. Alzheimer's disease is characterized by progressive cognitive decline, memory impairment, and various behavioral and functional disruptions. Its impact extends beyond the affected individuals, placing a substantial burden on caregivers and straining healthcare systems worldwide. The disease's underlying pathology involves the accumulation of abnormal protein aggregates, such as amyloid-beta plaques and tau tangles, in the brain, leading to neuronal dysfunction and cell death. Early diagnosis of Alzheimer's disease holds immense importance for several reasons. Firstly, interventions like pharmacological treatments and lifestyle adjustments are most effective when initiated during the early stages of the disease. Delayed diagnosis limits the potential benefits of these interventions. Secondly, early diagnosis allows affected individuals and their families to plan for the future, make informed decisions about care, and access support services promptly. However, diagnosing Alzheimer's disease in its earliest stages remains a significant challenge. The disease often remains undetected until symptoms become pronounced and substantial neuronal damage has occurred. Traditional diagnostic approaches rely on clinical assessments, cognitive tests, and neuroimaging, which may lack sensitivity and specificity for early detection.

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