Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models

R. Rohith; Sakthi Jaya Sundar Rajasekar; Thangavel Murugan; Varalakshmi Perumal

doi:10.3233/SHTI250684

Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models

R. Rohith
, Sakthi Jaya Sundar Rajasekar
, Thangavel Murugan
, Varalakshmi Perumal

Department of Information System and Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Alzheimer’s Disease (AD) is a neurodegenerative disorder that gradually deteriorates motor and cognitive abilities, including handwriting abilities. This study explores the effectiveness of handwriting analysis in detecting AD by leveraging Machine Learning (ML) techniques. A dataset containing handwriting samples was preprocessed using normalization and Synthetic Minority Over-Sampling Technique (SMOTE) to balance class distribution. Multiple ML models were trained and evaluated. Among the tested models, the highest classification accuracy, 99.26%, was attained by Multi-Layer Perceptron (MLP). The findings suggest that handwriting-based assessment, combined with advanced ML techniques, can serve as a promising non-intrusive tool for screening and evaluating the prognosis of AD.

Original language	English
Title of host publication	Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics
Editors	John Mantas, Arie Hasman, Parisis Gallos, Emmanouil Zoulias, Konstantinos Karitis
Publisher	IOS Press BV
Pages	116-120
Number of pages	5
ISBN (Electronic)	9781643686004
DOIs	https://doi.org/10.3233/SHTI250684
Publication status	Published - Jun 26 2025
Event	23rd Annual International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2025 - Athens, Greece Duration: Jul 4 2025 → Jul 6 2025

Publication series

Name	Studies in Health Technology and Informatics
Volume	328
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	23rd Annual International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2025
Country/Territory	Greece
City	Athens
Period	7/4/25 → 7/6/25

Keywords

Alzheimer’s Disease
Artificial Intelligence
Handwriting Analysis
Machine Learning
Multi-Layer Perceptron
Neurodegenerative disorders

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI250684

Cite this

Rohith, R., Rajasekar, S. J. S., Murugan, T., & Perumal, V. (2025). Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models. In J. Mantas, A. Hasman, P. Gallos, E. Zoulias, & K. Karitis (Eds.), Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics (pp. 116-120). (Studies in Health Technology and Informatics; Vol. 328). IOS Press BV. https://doi.org/10.3233/SHTI250684

Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models. / Rohith, R.; Rajasekar, Sakthi Jaya Sundar; Murugan, Thangavel et al.
Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics. ed. / John Mantas; Arie Hasman; Parisis Gallos; Emmanouil Zoulias; Konstantinos Karitis. IOS Press BV, 2025. p. 116-120 (Studies in Health Technology and Informatics; Vol. 328).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rohith, R, Rajasekar, SJS, Murugan, T & Perumal, V 2025, Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models. in J Mantas, A Hasman, P Gallos, E Zoulias & K Karitis (eds), Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics. Studies in Health Technology and Informatics, vol. 328, IOS Press BV, pp. 116-120, 23rd Annual International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2025, Athens, Greece, 7/4/25. https://doi.org/10.3233/SHTI250684

Rohith R, Rajasekar SJS, Murugan T, Perumal V. Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models. In Mantas J, Hasman A, Gallos P, Zoulias E, Karitis K, editors, Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics. IOS Press BV. 2025. p. 116-120. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI250684

Rohith, R. ; Rajasekar, Sakthi Jaya Sundar ; Murugan, Thangavel et al. / Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models. Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics. editor / John Mantas ; Arie Hasman ; Parisis Gallos ; Emmanouil Zoulias ; Konstantinos Karitis. IOS Press BV, 2025. pp. 116-120 (Studies in Health Technology and Informatics).

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abstract = "Alzheimer{\textquoteright}s Disease (AD) is a neurodegenerative disorder that gradually deteriorates motor and cognitive abilities, including handwriting abilities. This study explores the effectiveness of handwriting analysis in detecting AD by leveraging Machine Learning (ML) techniques. A dataset containing handwriting samples was preprocessed using normalization and Synthetic Minority Over-Sampling Technique (SMOTE) to balance class distribution. Multiple ML models were trained and evaluated. Among the tested models, the highest classification accuracy, 99.26\%, was attained by Multi-Layer Perceptron (MLP). The findings suggest that handwriting-based assessment, combined with advanced ML techniques, can serve as a promising non-intrusive tool for screening and evaluating the prognosis of AD.",

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AB - Alzheimer’s Disease (AD) is a neurodegenerative disorder that gradually deteriorates motor and cognitive abilities, including handwriting abilities. This study explores the effectiveness of handwriting analysis in detecting AD by leveraging Machine Learning (ML) techniques. A dataset containing handwriting samples was preprocessed using normalization and Synthetic Minority Over-Sampling Technique (SMOTE) to balance class distribution. Multiple ML models were trained and evaluated. Among the tested models, the highest classification accuracy, 99.26%, was attained by Multi-Layer Perceptron (MLP). The findings suggest that handwriting-based assessment, combined with advanced ML techniques, can serve as a promising non-intrusive tool for screening and evaluating the prognosis of AD.

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Enhanced Handwriting Kinematic Modeling for Alzheimer's Disease Classification Using Machine Learning Models

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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