IRO Journals
Journal of Innovative Image Processing

Explainable Artificial Intelligence with Deep Representation Learning Model for Polycystic Ovary Syndrome Detection Using Clinical Features

Open Access
Published: 11 July, 2026
Pages: 916-937
  • Veena Rani , Veena Rani
    Research Scholar, Department of Computer Science and Engineering, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
    Research Scholar, Department of Computer Science and Engineering, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
  • Chandrasekar Venkatachalam , Chandrasekar Venkatachalam
    Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
    Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
  • Karthick Raghunath K M. Karthick Raghunath K M.
    Associate Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
    Associate Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed - to - be University), Kanakapura Campus, Bangalore, Karnataka, India
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How to Cite

Rani, Veena, Chandrasekar Venkatachalam, and Karthick Raghunath K M. 2026. “Explainable Artificial Intelligence With Deep Representation Learning Model for Polycystic Ovary Syndrome Detection Using Clinical Features”. Journal of Innovative Image Processing 8 (3): 916-37. https://doi.org/10.36548/jiip.2026.3.009.
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Keywords

PCOS Detection
Deep Learning
FT-Transformer
Explainable Artificial Intelligence
Hybrid Feature Selection
Clinical Data

Abstract

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine condition affecting females and requires timely diagnosis for appropriate management and to mitigate long-term adverse problems. It is a complex condition identified by medical features such as hormonal imbalance, the existence of numerous ovarian follicles, irregular menstrual cycles, and anovulation. Over the past few years, Artificial Intelligence and deep learning approaches have demonstrated positive outcomes in medical applications by allowing effective analysis of intricate medical data. This study introduces an Explainable Artificial Intelligence-based Deep Representation Learning Model for Polycystic Ovary Syndrome Detection (XAIDRL-PCOSD). The proposed approach aims to support early and reliable detection of PCOS by leveraging real-world patient data. The model applies a hybrid feature selection strategy combining mutual information, recursive feature elimination, and LASSO to recognize the most significant clinical attributes. For classification, a feature tokenization transformer is employed to learn deep feature representations and capture complex relationships among clinical parameters. During training, the Ranger optimizer is utilized to enhance convergence and overall model performance. In addition, explainable artificial intelligence is incorporated using SHAP to provide clear insights into feature contributions, enhancing the interpretability of the model’s predictions for both PCOS and No PCOS cases. The experimental outcomes show that the proposed model accomplishes effective performance in PCOS detection with an accuracy of 95.71% while also offering transparency in decision-making. Therefore, the proposed XAIDRL-PCOSD model can serve as an effective tool in assisting medical professionals in understanding key factors influencing diagnosis.

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