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All Journal Ambidextrous : Journal of Innovation, Efficiency and Technology in Organization
Sabila Sulistyani
Program Studi Informatika Fakultas Teknik dan Informatika Universitas Bina Sarana Informatika
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Comparison of Machine Learning Algorithms for Polycystic Ovary Syndrome (PCOS) Classification Sabila Sulistyani; Sumanto; Rizal Amegia Saputra
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 5 No. 03 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v5i03.897

Abstract

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder in women of reproductive age that poses challenges in diagnosis and early detection. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression algorithms in classifying PCOS under consistent testing conditions. This study employs a quantitative approach and machine learning methods using secondary data from the PCOS Diagnosis dataset. The study population and sample consisted of 1,000 patient medical records, all of which were used, with 800 for training and 200 for testing. Altair AI Studio was used as the research tool, while analysis was conducted using confusion matrices, accuracy, precision, recall, and F1-score. The results show that KNN achieved an accuracy of 96.00%, precision of 90.91%, recall of 90.91%, and an F1-score of 90.91%, while SVM and Logistic Regression produced lower performance. In conclusion, KNN provided the most consistent classification performance on the research dataset and has the potential to be used as a supporting approach for PCOS classification based on clinical data.
Co-Authors Rizal Amegia Saputra Sumanto