A Hybrid Deep Learning Ensemble Framework for Accurate PCOS Prediction

Author(s)	S. Touseef, B. V. Hari Pratap Reddy
Country	India
Abstract	A prevalent hormonal condition that affects women of reproductive age, polycystic ovarian syndrome (PCOS) is linked to infertility, metabolic problems, and other health problems. To lower long-term hazards, early and precise diagnosis is crucial. But conventional diagnostic techniques are frequently laborious, subjective, and error-prone. Class imbalance, which can lower prediction sensitivity, is another issue that current machine learning techniques must deal with. This project combines Random Forest, 1D-CNN, and CNN-LSTM models to offer a Deep Learning–Enhanced Ensemble Framework for PCOS detection. SMOTEENN is a hybrid resampling approach used to address data imbalance. The suggested model outperformed current approaches with an accuracy of 99.11% and a recall of 100%. These findings demonstrate its efficacy as a trustworthy and precise screening method for early PCOS identification.
Keywords	PCOS detection, Deep Learning, metabolic issues, infertility, CNN, LSTM.
Field	Engineering
Published In	Volume 17, Issue 3, July-September 2026
Published On	2026-07-04

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