ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
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CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data
Authors
Tamil Elakya T , Dr.K.Manikandan
Keywords
Artificial Intelligence, Deep learning, Psychiatric Illness, Bipolar Disorder, Mental health.
Abstract
Bipolar disorder is a chronic and debilitating psychiatric illness characterized by recurrent episodes of mania and depression, significantly affecting an individual's emotional stability, cognitive functioning, and quality of life. Accurate diagnosis remains challenging due to symptom overlap with other mood disorders and reliance on subjective clinical assessments. This study presents a clinically driven deep learning framework for the automated prediction of bipolar disorder using a structured, symptom-based dataset reflecting common psychiatric indicators. Three deep learning models--Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)--were designed and evaluated to analyze behavioral and clinical symptom patterns. Standard preprocessing techniques, including normalization and train-test splitting, were applied to ensure reliable model performance. The predictive capability of each model was assessed using classification accuracy and confusion matrix analysis. Results demonstrate that deep learning approaches effectively model complex clinical relationships within psychiatric data. The ANN model achieved the highest predictive accuracy, indicating its suitability for tabular clinical datasets. This work emphasizes the potential of deep learning as a supportive clinical decision-making tool for early bipolar disorder screening and improved mental health assessment.
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IJCRT — journal style
"A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 3, page no.a69-a76, March 2026, Available :https://ijcrt.org/papers/IJCRT2603008.pdf
APA — 7th edition
T, T. E., & Dr.K.Manikandan. (2026). A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data. International Journal of Creative Research Thoughts, 14(3), a69-a76. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008
MLA — 9th edition
T, Tamil Elakya, and Dr.K.Manikandan. "A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data." International Journal of Creative Research Thoughts, vol. 14, no. 3, 2026, pp. a69-a76, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008.
Chicago — 17th, bibliography
T, Tamil Elakya, and Dr.K.Manikandan. "A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data." International Journal of Creative Research Thoughts 14, no. 3 (2026): a69-a76. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008.
Harvard — author–date
T, T.E. and Dr.K.Manikandan (2026) 'A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data', International Journal of Creative Research Thoughts, 14(3), pp. a69-a76. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008
IEEE — numbered reference
T. E. T and Dr.K.Manikandan, "A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data," IJCRT, vol. 14, no. 3, pp. a69-a76, Mar. 2026.
Vancouver — biomedical
T TE, Dr.K.Manikandan. A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data. IJCRT. 2026 Mar;14(3):a69-a76.
AMA — 11th edition
T TE, Dr.K.Manikandan. A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data. IJCRT. 2026;14(3):a69-a76. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2603008,
author = {Tamil Elakya T and Dr.K.Manikandan},
title = {A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {3},
pages = {a69-a76},
year = {2026},
month = {March},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - T, Tamil Elakya
AU - Dr.K.Manikandan
TI - A Comparative Deep Learning Architecture for Bipolar Disorder Prediction Using Symptom-Based Clinical Data
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 3
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008
SP - a69-a76
ER -
Issue
Volume 14 Issue 3,
March-2026
Pages : a69-a76
Article Information
- Pages
- a69-a76
- Published
- March 2026
- Registration ID
- IJCRT_301549
- Published Paper ID
- IJCRT2603008
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Coimbatore, Tamil Nadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 235
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2603008
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2603008.pdf
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International Journal of Creative Research Thoughts
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2320-2882
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The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
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