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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Abu Taher, Kazi
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Computer Science & IT Engineering

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Multilayer stacking for polycystic ovary syndrome diagnosis Abu Taher, Kazi; Ahmed, Samia; Ferdous Esha, Jannatul; Rahman, Md. Sazzadur; Sanwar Hosen, A. S. M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1968-1975

Abstract

Polycystic ovary syndrome (PCOS) is a complicated hormonal condition that is experienced by women. Despite extensive research, the precise reason be hind PCOS remains unknown, and effective treatments are still lacking. Thus, early diagnosis and treatment have a significant positive impact on the health of women. Recently, there has been remarkable performance demonstrated by machine learning (ML)-based detection models for PCOS identification. They are fast and low cost compared to the traditional processes. In this work, a multi stacking PCOS detection model is proposed using K-fold cross validation. The model uses three different ML algorithms namely: na¨ıve Bayes (NB), ran dom forest (RF), and logistic regression (LR) as base classifiers and a neural network, multi-layer perception (MLP) as meta model. This approach utilizes two feature selection techniques and compares the performances on the stack ing methods. Among the two feature selection techniques, Pearson correlation approach performed better with average 98.79% accuracy, 99.17% sensitivity, 98.40% specificity, and 98.79% f1-score.
Co-Authors Ahmed, Samia Ferdous Esha, Jannatul Rahman, Md. Sazzadur Sanwar Hosen, A. S. M.