Alzheimer’s Disease Prediction System: A Data-Driven Approach

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting millions globally.Early detection is crucial for timely intervention and improved care.This paper proposes a novel approach for AD prediction through hand-writing analysis using machine learning (ML).Handwriting data is collected and preprocessed to extract features such as pen pressure, stroke velocity, cur-vature, and letter spacing.These features are used to train and evaluate ML models for classifying handwriting patterns associated with AD.The system is assessed using metrics like accuracy, precision, recall, and F1 score.Experimental results demonstrate the model's ability to differentiate between healthy individ-uals and AD patients, showcasing its potential as a non-invasive, cost-effective, and scalable early diagnostic tool.This handwriting-based approach offers a promising solution for remote screening and monitoring of AD, ensuring broader accessibility.