Dynamic Memory-Augmented Whale Optimization Algorithm (DMA-WOA) as Feature Descriptor for Polycystic Ovary Syndrome Detection

Abstract

This study introduces a dynamically memory-adjusted whale optimization algorithm (DMA-WOA) for feature selection in polycystic ovary syndrome (PCOS) diagnosis. To overcome the standard WOA's limitations in balancing exploration and exploitation, DMA-WOA incorporated adaptive memory control to improve convergence stability and computational efficiency. In DMA-WOA adaptive control dynamics adjusted memory size and influence based on population diversity and fitness change, enabling consistent convergence in high-dimensional clinical data. The framework was evaluated on the only publicly available PCOS electronic health records dataset using diverse classifiers, including SVM, RF, LR, MLP, RNN, LSTM, GRU, TabTransformer, and TabNet. Results showed that DMA-WOA achieved superior accuracy, generalization, and runtime efficiency compared to baseline and standard WOA approaches, while comparative analysis with existing metaheuristics confirmed its enhanced optimization robustness and diagnostic reliability.