Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease

Cansu Akyürek Anacur; Asuman Günay Yılmaz; Bekir Dizdaroğlu

doi:10.17798/bitlisfen.1843864

Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease

Abstract

The study of handwriting behavior is becoming an increasingly important method for assessing neurodegenerative disorders. In this study, a feature set was created by recalculating 18 features (including temporal, kinematic, and geometric characteristics) defined in the literature from raw data in the DARWIN handwriting database. The resulting representations was initially evaluated using a 1-Dimensional Convolutional Neural Network (1D-CNN) to investigate the information provided by handwriting patterns for the detection of Alzheimer's disease. In the second stage, this feature set was converted into a two-dimensional image format, and the visual representations were further analyzed using the 2D Convolutional Neural Network (2D-CNN) model. The 1D-CNN model developed on numerical data achieved 94.29% accuracy, while the 2D-CNN model trained on two-dimensional visual representations achieved 91.43% accuracy. The findings indicate that numerical and visual representations derived from handwriting data contain significant discriminatory information for the classification of Alzheimer's disease and that both approaches exhibit similar and comparable levels of performance.

Keywords

Ethical Statement

The study is complied with research and publication ethics.

Thanks

The authors like to convey their heartfelt appreciation to the DARWIN Research Team for their gracious provision of the raw data of the DARWIN handwriting dataset used in this work. Their essential assistance and involvement were crucial to the effective completion of this study.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Cansu Akyürek Anacur ^*
0009-0005-8608-7892
Türkiye

Asuman Günay Yılmaz
0000-0003-3960-5085
Türkiye

Bekir Dizdaroğlu
0000-0002-2955-1776
Türkiye

Publication Date

June 30, 2026

Submission Date

December 17, 2025

Acceptance Date

May 12, 2026

Published in Issue

Year 2026 Volume: 15 Number: 2

DOI

https://doi.org/10.17798/bitlisfen.1843864

IZ

https://izlik.org/JA82TF44WT

Cite

RIS / Bibtex

APA

Akyürek Anacur, C., Günay Yılmaz, A., & Dizdaroğlu, B. (2026). Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(2), 915-927. https://doi.org/10.17798/bitlisfen.1843864

AMA

1.Akyürek Anacur C, Günay Yılmaz A, Dizdaroğlu B. Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(2):915-927. doi:10.17798/bitlisfen.1843864

Chicago

Akyürek Anacur, Cansu, Asuman Günay Yılmaz, and Bekir Dizdaroğlu. 2026. “Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (2): 915-27. https://doi.org/10.17798/bitlisfen.1843864.

EndNote

Akyürek Anacur C, Günay Yılmaz A, Dizdaroğlu B (June 1, 2026) Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 2 915–927.

IEEE

[1]C. Akyürek Anacur, A. Günay Yılmaz, and B. Dizdaroğlu, “Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, pp. 915–927, June 2026, doi: 10.17798/bitlisfen.1843864.

ISNAD

Akyürek Anacur, Cansu - Günay Yılmaz, Asuman - Dizdaroğlu, Bekir. “Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/2 (June 1, 2026): 915-927. https://doi.org/10.17798/bitlisfen.1843864.

JAMA

1.Akyürek Anacur C, Günay Yılmaz A, Dizdaroğlu B. Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:915–927.

MLA

Akyürek Anacur, Cansu, et al. “Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 2, June 2026, pp. 915-27, doi:10.17798/bitlisfen.1843864.

Vancouver

1.Cansu Akyürek Anacur, Asuman Günay Yılmaz, Bekir Dizdaroğlu. Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Jun. 1;15(2):915-27. doi:10.17798/bitlisfen.1843864