ML Powered Handwriting Analysis for Early Detection of Alzheimer’s disease
Keywords:
Alzheimer's Disease, Handwriting analysis, Machine Learning, Early detection, ExtraTreesClassifier, Random Forest Classifier, XGBClassifier, Cognitive decline, Python, Frontend developmentAbstract
The project entitled "ML-Powered Handwriting Analysis for Early Detection of Alzheimer's Disease" involves the use of machine learning techniques in detecting the early signs of cognitive decline influenced by handwriting patterns. The handwriting does collection of samples and extracts important characteristics like pressure applied during strokes, spacing, and curvature, then uses classification algorithms to represent changes with respect to the disease of Alzheimer's. The features are later trained by models like ExtraTreesClassifier, RandomForestClassifier and XGBClassifier to get better accuracy in prediction at the primary stage. Backend processing is done using Python and the front end makes use of HTML, CSS, and JavaScript for a user-friendly interface allowing easy submission of handwriting samples and display of results. This novel take on screening is unobtrusive, easy, and cost-effective for the early screening of possible Alzheimer's patients thus possibly assisting healthcare personnel in diagnosing early intervention methods.
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Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
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