Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Enhanced Prediction of Polycystic Ovary Syndrome using Machine Learning Model

Authors
P. Hemalatha1, M. Prasanna Lakshmi1, *, M. Venkata Rao2
1Department of Computer Applications, Siddhartha Academy of Higher Education, Deemed to be University, Vijayawada, 520007, Andhra Pradesh, India
2Department of Mathematics, Siddhartha Academy of Higher Education, Deemed to be University, Vijayawada, 520007, Andhra Pradesh, India
*Corresponding author. Email: mplakshmi@vrsiddhartha.ac.in
Corresponding Author
M. Prasanna Lakshmi
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_4How to use a DOI?
Keywords
Polycystic Ovary Syndrome (PCOS); Machine Learning; Cat Boost; Infertility Diagnosis; Feature Importance; Clinical Decision Support
Abstract

PCOS which stands for Polycystic ovary syndrome is a very common endocrine disorder which affects women of childbearing age and which also causes infertility, biochemical abnormalities like insulin resistance and obesity, and serious psychosocial stress. Early diagnosis and intervention is key to good management of the disease; but also in this area we are limited by the fact that traditional diagnostic methods do not always live up to the mark which is due to the complex and multi factorise nature of PCOS. What we are seeing now is an increase in the available clinical and biochemical data which in turn is bringing in large scale opportunity for use of machine learning (ML) techniques which in turn we think have great promise in improving diagnostic accuracy and predictive outcomes. In this work we report on the in-depth study of many ML algorithms which include LG-Logistic Regression, RF-Random Forest, SVM-Support Vector Machines, and also GB-Gradient Boosting and we also look at performance of the Cat Boost classifier.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_4How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Hemalatha
AU  - M. Prasanna Lakshmi
AU  - M. Venkata Rao
PY  - 2026
DA  - 2026/06/16
TI  - Enhanced Prediction of Polycystic Ovary Syndrome using Machine Learning Model
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 23
EP  - 32
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_4
DO  - 10.2991/978-94-6239-693-7_4
ID  - Hemalatha2026
ER  -