Polycystic Ovary Syndrome (PCOS) Prediction System Using PSO-SVM

Lukman Hakim Shaufee; Hamidah Jantan; Ummu Fatihah Mohd Bahrin

doi:10.24191/jcrinn.v9i1.414

Polycystic Ovary Syndrome (PCOS) Prediction System Using PSO-SVM

Authors

Lukman Hakim Shaufee College of Computing, Informatics & Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia.
Hamidah Jantan College of Computing, Informatics & Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia.
Ummu Fatihah Mohd Bahrin College of Computing, Informatics & Mathematics, Universiti Teknologi MARA Terengganu Branch, Kuala Terengganu Campus, Terengganu, Malaysia.

DOI:

https://doi.org/10.24191/jcrinn.v9i1.414

Keywords:

PCOS, PSO-SVM, Feature Selection, Improved SVM, Machine Learning

Abstract

A prevalent and complicated gynaecological condition that affects women’s reproductive health is PCOS. However, delayed diagnosis and treatment are frequently caused by a lack of understanding of its signs and symptoms. To help users and specialized physicians identify and anticipate ovarian cysts early, a PCOS prediction system integrating PSO-SVM was created to solve this issue. This study explores the application of data mining techniques, using PSO-SVM, to predict PCOS in the field of gynaecology. The dataset was taken from the Kaggle benchmark dataset, owned by Karnika Kapoor. There are 42 selected features and attributes of the PCOS dataset. The system used Python-based data preprocessing, data splitting, and PSO-SVM optimization for predicting PCOS disease. The evaluation showed that PSO-SVM with 20 particles and 100 iterations achieved the best accuracy for feature selection with an accuracy of 90.18%. The system exhibited promising predictive abilities. To enhance accuracy and user experience, future work should focus on longitudinal data integration, expert decision support, and collaboration with medical experts. The developed PSO-SVM-based PCOS prediction system significantly improves risk assessment and early identification, aiding patients, and medical practitioners. It serves as a valuable decision support tool for doctors, enabling quick and accurate diagnosis for early intervention and specialized treatment plans.

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