Alzheimer’s disease (AD) is a neurodegenerative condition that imposes significant emotional and financial burdens on patients, caregivers, and healthcare systems. Mild cognitive impairment (MCI) is often a precursor to AD, making early diagnosis crucial for managing dementia and slowing disease progression. However, current diagnostic methods struggle to integrate diverse risk factors, including genetics, age, family history, lifestyle, environment, comorbidities, mental health, and emerging AD biomarkers. This research applies machine learning to combine these heterogeneous factors and predict the progression from MCI to AD. The findings could contribute to earlier interventions and reduce the financial strain of AD on healthcare systems.
