Early diagnosis of polycystic ovarian syndrome (PCOS) using machine learning: An ensemble learning approach

Shivam Krishana, Sparsh Sharma, Saurabh Singh, Byungun Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent years, women's reproductive health has become a significant concern. Issues like preterm abortions, infertility, ovarian disorders, and declining fertility rates are prevalent. Polycystic Ovarian Syndrome (PCOS), a common reproductive disorder, often leads to infertility due to irregular cycles and elevated androgens. Despite its unclear cause and cure, early detection and intervention are vital. Researchers are exploring AI-based diagnostics to expedite diagnosis and mitigate clinical challenges. Our methodology revolves around leveraging non-invasive parameters to construct a feature vector optimized for machine learning algorithms. We utilize Principal Component Analysis (PCA) as a crucial step for dimensionality reduction, which streamlines the dataset's representation while retaining vital information. To bolster the accuracy of PCOS diagnosis, we deploy a majority voting ensemble model that incorporates five base models. This ensemble approach not only enhances classification precision but also addresses issues like overfitting and model robustness, making it especially valuable when dealing with datasets of limited size. The accuracy, precision, f1-score, and recall for the suggested model are found to be 84.3%, 76.1%, 81%, and 84.3% respectively. Our research shows that our ensemble model performs better overall and across individual classes than the fundamental models. This development represents a substantial advancement in the field of PCOS diagnosis, showcasing the pivotal role of machine learning in enhancing diagnostic precision.

Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
EditorsJagadish Nayak, Vilas H Gaidhane, Nilesh Goel
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-312
Number of pages6
ISBN (Electronic)9798350393415
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023 - Dubai, United Arab Emirates
Duration: 7 Dec 20239 Dec 2023

Publication series

NameProceedings of IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

Conference

Conference2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period7/12/239/12/23

Keywords

  • ensemble learning
  • polycystic ovarian syndrome
  • principal component analysis

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