Amirkabir University of Technology AUT Journal of Mathematics and Computing 2783-2449 6 3 2025 07 01 Machine learning approach for bipolar disorder analysis and recognition based on handwriting digital images 223 239 5383 10.22060/ajmc.2024.22576.1176 EN Ahmadali Jamali Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran Reza Kargar Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran Shahin Alipour Department of Biomedical Engineering, University of Houston, USA Mohsen Rostamy-Malkhalifeh Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran Journal Article 2023 07 24 In some cases, handwriting is a manifestation of the human mind, and it can reveal various psychological characteristics and mental disorders. Among these disorders, bipolar disorder is a well-known and widely studied condition in cognitive science and psychotherapy, and it can be detected in handwriting. In this research, we applied image processing techniques to analyze the handwriting characteristics of people with bipolar disorder based on their responses to a survey. We also proposed a machine learning model that can classify whether a person has bipolar disorder or not by using their handwriting as an input. https://ajmc.aut.ac.ir/article_5383_bdf74682a7b24bfd486bd06c6d9effc2.pdf