Alzheimer’s disease (AD) is a progressive neurological disorder that affects memory, thinking, and behavior. Early detection is crucial for improving patient care and management, but it remains a significant challenge due to the complexity of contributing factors and the long progression period of the disease. This project aims to develop a predictive model to assess the likelihood of an individual having Alzheimer's disease based on a range of features, including demographic details, medical history, lifestyle factors, clinical measurements, and cognitive assessments.

KPIs:
Model accuracy and performance
Early detection success rate
Reliable probabilistic model for risk prediction

Stakeholders:
Healthcare professionals - Patients and families - Medical researchers - Pharmaceutical companies - Public health organizations