Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

Authors

  • Neha Yadav Lovely Professional University image/svg+xml
  • Ranjith Kumar A Lovely Professional University image/svg+xml
  • Sagar Dhanraj Pande Pimpri Chinchwad University

DOI:

https://doi.org/10.4108/eetpht.10.5552

Keywords:

Polycystic Ovary Syndrome, RSCV, GSCV, BO, Optuna, TPOT

Abstract

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods.

OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage.

METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms.

RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively.

CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS. 

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References

WHO:https://www.who.int/news-room/fact-sheets/detail/polycystic-ovary-syndrome

ENDO 2023: Chicago IL [2023]

https://www.contemporaryobgyn.net/view/getting-a-grip-on-polycystic-ovary-syndrome

Getting a grip on polycystic ovary syndrome: Jessica L.Chan, MD [2023]: https://www.contemporaryobgyn.net/view/getting-a-grip-on-polycystic-ovary-syndrome

Amsy Denny, Anita Raj and Ashi Ashok : “i-HOPE: Detection And Prediction System For Polycystic Ovary Syndrome (PCOS) Using Machine Learning Techniques”.

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal: “Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms”

Preeti Chaunhan, Pooja Patil, Neha Rane: “Comparative Analysis of Machine Learning Algorithms for Prediction of PCOS”.

P. Mehrotra et al. “Automated screening of Polycystic Ovary Syndrome using machine learning techniques”.

Anuradha, D.T., & Priyanka R. L., Genetic Clustering for Polycystic Ovary Syndrome Detection in Women of Reproductive Age

K. Meena, M. Manimekalai, and S. Rethinavalli. “Correlation of Artificial Neural Network Classification and NFRS attribute Filtering algorithm for PCOS data”

Namrata Tanwani : “Detecting PCOS using Machine Learning”

Satish C. R Nandipati, Chew XinYing and Khaw Khai Wah: Polycystic Ovarian Syndrome (PCOS) Classification and Feature Selection by Machine Learning Techniques

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Published

26-03-2024

How to Cite

1.
Yadav N, A RK, Pande SD. Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 26 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5552