Predicting Depression and Anxiety in Women Using LDA-Based ‎CNN

  • Authors

    • Dr. Megala Rajendran Vice-Rector, Research & Innovation, Turan International University, Namangan, Uzbekistan
    • Dr. V. Sridevi Assistant Professor, Department of Computer Science, PSG College of Arts and Science, Coimbatore
    • Dr. Kamalraj Subramaniam Professor and Head, Department of Biomedical Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, India
    • K.B. Manikanta Assistant Professor, Sri Venkateshwara College of Engineering, Bengaluru
    • R. Udayakumar Professor & Director, Kalinga University, India
    https://doi.org/10.14419/exjz5950

    Received date: June 10, 2025

    Accepted date: June 17, 2025

    Published date: November 1, 2025

  • Women; Depression; Mental Health Issues; Medical Application; Artificial Intelligence; Deep Learning Algorithm

  • Abstract

    Depression affects women of all ages in India. The stress of balancing many roles can lead to depression in Indian women, which goes ‎untreated due to social stigma. Among the most prevalent mental health conditions that affect women in their reproductive years are ‎premenstrual dysphoric syndrome, postpartum depression, and PMS. Primary care physicians should emphasize early diagnosis of intimate ‎relationships and domestic abuse and mandate routine tests for these issues. Since antidepressants constitute the cornerstone of treatment, ‎they ought to be freely available at all primary care levels. When individuals take their medication as directed for a sufficient period ‎and keep in regular contact with mental health professionals, the best possible results are obtained. The best results are obtained when ‎cognitive therapy is used in conjunction with other non-pharmacological methods. In this work, deep learning architectures using Linear ‎Discriminant Analysis (LDA) were utilized. Possible contributions to the studies include convolutional neural networks (CNNs) and ‎transformer-based pre-trained language models for classification. When the six functional status groups are utilized rather than just one set ‎of depressed symptoms, the results will be more dependable and consistent‎.

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  • How to Cite

    Rajendran, D. M. ., Sridevi, D. V. ., Subramaniam, D. K. ., Manikanta, K. ., & Udayakumar, R. . (2025). Predicting Depression and Anxiety in Women Using LDA-Based ‎CNN. International Journal of Basic and Applied Sciences, 14(SI-1), 488-494. https://doi.org/10.14419/exjz5950