Predicting polycystic ovary syndrome using SVM

Tanvir Mahmud 1, 2, * and S A Sabbirul Mohosin Naim 3

1 Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, Bangladesh.
2 Department of Electrical and Computer Engineering, Lamar University, Beaumont, Texas 77710, USA.
3 Department of Electrical Engineering, School of Engineering, San Francisco Bay University, Fremont, CA, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 4400-4408.
Article DOI: 10.30574/ijsra.2024.13.2.2153
Publication history: 
Received on 28 September 2024; revised on 18 December 2024; accepted on 21 December 2024
 
Abstract: 
Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healthcare systems to reduce the disease’s problems and complications. Machine learning (ML) and ensemble learning have recently shown promising results in medical diagnostics. The main goal of our research is to provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations. Feature selection methods with different types of SVM models are used to get optimal feature selection and best model.
 
Keywords: 
Polycystic Ovarian Syndrome; Machine Learning; PCOS Predict; Support Vector Machine; linear SVM; RBF; polynomial SVM
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0