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Article

A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome

by
Varada Vivek Khanna
1,
Krishnaraj Chadaga
2,
Niranajana Sampathila
1,*,
Srikanth Prabhu
2,*,
Venkatesh Bhandage
2 and
Govardhan K. Hegde
2
1
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
2
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
*
Authors to whom correspondence should be addressed.
Appl. Syst. Innov. 2023, 6(2), 32; https://doi.org/10.3390/asi6020032
Submission received: 29 January 2023 / Revised: 19 February 2023 / Accepted: 21 February 2023 / Published: 23 February 2023
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Abstract

Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.
Keywords: deep learning; explainable artificial intelligence; local Interpretable model explainer; shapley additive values; machine learning; polycystic ovary syndrome deep learning; explainable artificial intelligence; local Interpretable model explainer; shapley additive values; machine learning; polycystic ovary syndrome

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MDPI and ACS Style

Khanna, V.V.; Chadaga, K.; Sampathila, N.; Prabhu, S.; Bhandage, V.; Hegde, G.K. A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Appl. Syst. Innov. 2023, 6, 32. https://doi.org/10.3390/asi6020032

AMA Style

Khanna VV, Chadaga K, Sampathila N, Prabhu S, Bhandage V, Hegde GK. A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Applied System Innovation. 2023; 6(2):32. https://doi.org/10.3390/asi6020032

Chicago/Turabian Style

Khanna, Varada Vivek, Krishnaraj Chadaga, Niranajana Sampathila, Srikanth Prabhu, Venkatesh Bhandage, and Govardhan K. Hegde. 2023. "A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome" Applied System Innovation 6, no. 2: 32. https://doi.org/10.3390/asi6020032

APA Style

Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., Bhandage, V., & Hegde, G. K. (2023). A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Applied System Innovation, 6(2), 32. https://doi.org/10.3390/asi6020032

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