Citation :

V. Lakshmi, B. Pushpa, "Explainable Deep Tabular Learning with SHAP-Guided Feature Optimization and Squeeze-Excitation Enhanced TabNet for Polycystic Ovary Syndrome Classification," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 19-36, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P102

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most prevalent reproductive endocrinopathies in women of reproductive age. This requires diagnostic systems that reliable and interpretable. This paper presents a deep tabular framework that like to maintain efficiency in the classification of Polycystic Ovarian Syndrome (PCOS) through effective data pre-processing, synthetic balancing of data, and optimizing for significant features. Initially, the clinical dataset is preprocessed by iterative imputation, outlier handling, feature normalization and synthetic balancing for statistical consistency and robust model learning. The study uses Conditional Tabular GAN (CTGAN) to augment the sample diversity and address class imbalance by generating additional minority samples. Moreover, a Transformer-based feature extractor captures associations between features to encode better reference feature embeddings. Then, performing Shapley Additive Explanations (SHAP)-guided feature selection using an XGBoost explainer to determine what predictors are most important in the model performance, hence improving both interpretability and computational efficiency. The TabNet-SE ahead of running the baseline model, i.e.Random Forest (RF) and XGBoost, in addition both conducting of these steps with the complete feature set and also SHAP-selected features. The experimental results show that TabNet-SE consistently outperforms all baseline models in terms of accuracy, precision, recall and F1-score when trained on the set of features optimized with SHAP. Model performance was further corroborated with confusion matrix, Receiver Operating Characteristic (ROC) curve, and other evaluation metrics. This framework not only facilitates accurate diagnosis of PCOS but also enhances interpretability, thus bridging the gap between clinical requirements and model transparency.

Keywords

Conditional Tabular GAN (CTGAN), Explainable Deep Learning, Medical data classification, Polycystic Ovary Syndrome (PCOS), SHAP-based Feature Selection, Squeeze-Excitation Network, TabNet-SE, Transformer feature extraction.

References

[1] Francisco J. Barrera et al., “Application of Machine Learning and Artificial Intelligence in the Diagnosis and Classification of Polycystic Ovarian Syndrome: A Systematic Review,” Frontiers in Endocrinology, vol. 14, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Zahra Zad et al., “Predicting Polycystic Ovary Syndrome with Machine Learning Algorithms from Electronic Health Records,” Frontiers in Endocrinology, vol. 15, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Abrar Alamoudi et al. “A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS),” Applied Computational Intelligence and Soft Computing, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Nikos Fazakis et al., “Iterative Robust Semi-Supervised Missing Data Imputation,” IEEE Access, vol. 8, pp. 90555-90569, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Lei Xu et al., “Modeling Tabular data using Conditional GAN,” arxiv preprint, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Sercan Ö. Arik, and Tomas Pfister, “TabNet: Attentive Interpretable Tabular Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6679-6687, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Chiranjibi Shah, Qian Du, and Yan Xu, “Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification,” Remote Sensing, vol. 14, no. 3, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Scott M. Lundberg, and Su-In Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, vol. 30, 2017.
[Google Scholar]

[9] Mehtap Agirsoy, and Matthew A. Oehlschlaeger, “A Machine Learning Approach for Non-Invasive PCOS Diagnosis from Ultrasound and Clinical Features,’ Scientific Reports, vol. 15, no. 1, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Kogilavani Shanmugavadivel et al., “Optimized Polycystic Ovarian Disease Prognosis and Classification using AI based Computational Approaches on Multi-Modality Data,” BMC Medical Informatics and Decision Making, vol. 24, no. 1, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Neha Yadav, Ranjith Kumar A, and Sagar Dhanraj Pande, “Comparative Analysis of Polycystic Ovary Syndrome Detection using Machine Learning Algorithms,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 10, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Oviya Graselin S et al., “Development of a Machine Learning Model to Classify Polycystic Ovarian Syndrome,” Technology and Health Care, vol. 33, no. 3, pp. 1478-1488, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Md Mahbubur Rahman et al., “Empowering Early Detection: A Web-based Machine Learning Approach for PCOS Prediction,” Informatics in Medicine Unlocked, vol. 47, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Hela Elmannai et al., “Polycystic Ovary Syndrome Detection Machine Learning Model based on Optimized Feature Selection and Explainable Artificial Intelligence,” Diagnostics, vol. 13, no. 8, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Pooja Balagouda Patil et al., “Explainable Ensemble-based Machine Learning Model for Polycystic Ovary Syndrome Detection using Hybrid Feature Selection,” International Journal of Information Technology, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Wenxiu Chen et al., “Development of Machine Learning Models for Diagnostic Biomarker Identification and Immune Cell Infiltration Analysis in PCOS,” Journal of Ovarian Research, vol. 18, no. 1, pp. 1-16, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Rebecca H.K. Emanuel et al., “What do users in a Polycystic Ovary Syndrome (PCOS) Forum Think About the Treatments They Tried: Analysing Treatment Sentiment using Machine Learning,” Physical and Engineering Sciences in Medicine, vol. 48, no. 2, pp. 723-741, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Eugenia Papadaki, Aristidis G. Vrahatis, and Sotiris Kotsiantis, “Exploring Innovative Approaches to Synthetic Tabular Data Generation,” Electronics, vol. 13, no. 10, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Vasileios C. Pezoulas, “Synthetic Data Generation Methods in Healthcare: A Review on Open-Source Tools and Methods,” Computational and Structural Biotechnology Journal, vol. 23, pp. 2892-2910, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[20] M. Kannan et al., “An Enhancement of Machine Learning Model Performance in Disease Prediction with Synthetic Data Generation,” Scientific Reports, vol. 15, no. 1, pp. 1-21, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Mikel Hernandez et al., “Comprehensive Evaluation Framework for Synthetic Tabular Data in Health: Fidelity, Utility and Privacy Analysis of Generative Models with and without Privacy Guarantees,” Frontiers in Digital Health, vol. 7, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Malak Alqulaity, and Po Yang, “Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-based Cardiovascular Healthcare,” Sensors, vol. 24, no. 23, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Ha Ye Jin Kang, Minsam Ko, and Kwang Sun Ryu, “Tabular Transformer Generative Adversarial Network for Heterogeneous Distributions in Healthcare,” Scientific Reports, vol. 15, no. 1, pp. 1-12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Ahmed Fahim et al., “A Hybrid Deep Learning Framework based on CNN-GRU-TabNet for the Predictive Modeling of COVID-19 Mortality,” Engineering Technology and Applied Science Research, vol. 15, no. 5, pp. 28057-28062, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Jiekee Lim et al., “Machine Learning Classification of Polycystic Ovary Syndrome based on Radial Pulse Wave Analysis,” BMC Complementary Medicine and Therapies, vol. 23, no. 1, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Bharti Panjwani et al., “Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women,” Sensors, vol. 25, no. 4, pp. 1-30, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Kaggle, PCOS Dataset, Kaggle, 2020. [Online]. Available: Https://Www.Kaggle.Com/Datasets/Shreyasvedpathak/Pcos-Dataset