Deep Learning - Enhanced Ensemble for PCOS Prediction

Author(s)	C. Ankitha, K. Neeharika
Country	India
Abstract	Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder affecting women of reproductive age and is associated with metabolic issues, infertility, and other health complications. Early and accurate diagnosis is important to reduce long-term risks. However, traditional diagnostic methods are often slow, subjective, and prone to errors. Existing machine learning approaches also face challenges such as class imbalance, which can reduce prediction sensitivity. This project proposes a Deep Learning–Enhanced Ensemble Framework for PCOS detection by combining Random Forest, 1D-CNN, and CNN-LSTM models. To handle data imbalance, SMOTEENN is used as a hybrid resampling technique. The proposed model achieved an accuracy of 99.11% and a recall of 100%, outperforming existing methods. These results highlight its effectiveness as a reliable and accurate screening tool for early PCOS detection.
Keywords	PCOS detection, Deep Learning, metabolic issues, infertility, CNN ,LSTM.
Discipline	Engineering
Published In	Volume 4, Issue 3, May-June 2026
Published On	2026-06-04

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Advanced International Journal of Multidisciplinary Research