Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied AI Letters

DOI: 10.1002/ail2.70016

Authors: Daniel Kwame Amissah, Leonard Mensah Boante, Solomon Mensah, Ebenezer Owusu, Justice Kwame Appati

Institution: University of Ghana

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,420