Handwriting Analysis for Early Detection of Alzheimer’s Using Machine Learning

Alzheimer’s Disease (AD) is a progressive disorder that slowly affects memory, reasoning, and motor abilities. Detecting the condition early is crucial, but commonly used diagnostic procedures—such as brain scans and fluid analysis—are costly, invasive, and often unable to capture the earliest signs of decline. Handwriting, which relies on coordinated cognitive and motor processes, has emerged as a promising non-invasive indicator of early impairment. This study reviews current handwriting-based diagnostic approaches and examines how features such as pressure variation, stroke behavior, tremor patterns, and timing inconsistencies reveal subtle symptoms of AD. A comparison of traditional analytical methods and modern machine-learning techniques is presented, along with an evaluation of their strengths, limitations, and feature- extraction strategies. The review also identifies research gaps and suggests the integration of handwriting data with additional digital biomarkers to support more effective, scalable screening tools for early Alzheimer’s detection.