Syed Tangim Pasha
- Collaborator, HCI Wing
Lecturer
Department of Computing and Information System
Daffodil International University
Research Interests
Health Informatics, Affective Computing, Digital Health, Wearable Computing
Conference Paper2025Cited by 1
Integrating Supervised and Self-Supervised Models for an Enhanced PCOS Detection: A Data-Driven Approach with Machine Learning Perspective
Tasmia Tahmida, Syed Tangim Pasha, Mehedi Hassan, Ashraful Islam, M. Ashraful Amin
2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)
In: 2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC)
IEEE, pp. 499-503
Abstract
Polycystic Ovary Syndrome (PCOS) is a common ovarian dysfunction that leads to various difficulties, including missed or irregular menstrual periods, acne, depression, mood swings, and excessive facial hair. Due to the wide range of symptoms, diagnosing PCOS can be challenging. In addition, the process can be costly, and there is a high rate of false positives in PCOS diagnoses. Machine Learning (ML) can aid both patients and clinicians in diagnosing PCOS by using labeled data to analyze patient history effectively. However, the Self-Supervised model uses labeled and unlabeled data to understand new patient cases better. Incorporating unlabeled information during model training has become increasingly reliable for identifying a new patient case study. In this study, we employed the publicly available KagglePCOS tabular dataset to evaluate various modeling approaches. Our findings indicated that the Support Vector Classifier (SVC) model achieved a commendable accuracy of$\mathbf{9 3. 8 \%}$. In comparison, the Gaussian Naive Bayes (GNB) model exhibited notably lower performance, with an accuracy of 54.1%. Among the Self-Supervised models, the Autoencoder showcased the most effective balance between accuracy and recall, achieving an accuracy of$\mathbf{8 2. 7 \%}$. Although other models, such as Simple Contrastive Learning of Representations (SimCLR) and Bootstrap Your Own Latent (BYOL), demonstrated lower F1 scores and accuracy, they highlighted the potential of Self-Supervised methods in identifying new cases within unlabeled datasets. While traditional Supervised models remain highly effective, SelfSupervised learning offers a promising direction, especially for diagnosing cases where labeled data is unavailable.
Keywords
Perspective (graphical)Computer scienceMachine learningArtificial intelligenceSupervised learning
CCDS Authors
Mehedi Hassan, PhD
- Collaborator, HCI Wing
Instructor
+1 more affiliationUniversity of Arkansas
Ashraful Islam, PhD
- Center Director, CCDS
- Director, HCI Wing
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)Research Interests
Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact
M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
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Cited By
- 1.Mathu Mozhi S P, Nithyasri B, Hepsi Ajibah A S. (2026). Mamba-SHAP Hybrid Framework for Explainable PCOS Detection from Clinical and Hormonal Data. , 1–7[10.1109/icbdml68582.2026.11544669]