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<p>Breast cancer is a leading cause of mortality worldwide, emphasizing the need for accurate and timely prediction of risk factors. This study proposes a novel approach using deep learning algorithms to predict breast cancer. The self-adaptive sea lion optimization-based recurrent neural network (SA-SLnO-RNN) is developed to select optimal features that enhance prediction accuracy. Evaluated on the breast cancer Wisconsin diagnostic (BCWD) dataset, the SA-SLnO-RNN achieves superior performance compared to existing methods, with accuracy, precision, recall, specificity, and F1-score values of 98.82%, 98.78%, 98.71%, 99.06%, and 98.75%, respectively. The proposed model’s self-adaptive nature prevents overfitting, while the RNN with optimization algorithm enables quick convergence and captures complex temporal dependencies, ensuring early and accurate breast cancer prediction.</p>

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Enhanced Breast Cancer Prediction Using Self-Adaptive Sea Lion Optimization-Based Recurrent Neural Network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • G. Santhosh Kumar,
  • C. A. Lath,
  • K. R. Pradeep,
  • M. Niranjanamurthy,
  • Anurag Sinha,
  • Omar Alqahtani,
  • Gran Badshah,
  • Saifullah Khalid

摘要

Breast cancer is a leading cause of mortality worldwide, emphasizing the need for accurate and timely prediction of risk factors. This study proposes a novel approach using deep learning algorithms to predict breast cancer. The self-adaptive sea lion optimization-based recurrent neural network (SA-SLnO-RNN) is developed to select optimal features that enhance prediction accuracy. Evaluated on the breast cancer Wisconsin diagnostic (BCWD) dataset, the SA-SLnO-RNN achieves superior performance compared to existing methods, with accuracy, precision, recall, specificity, and F1-score values of 98.82%, 98.78%, 98.71%, 99.06%, and 98.75%, respectively. The proposed model’s self-adaptive nature prevents overfitting, while the RNN with optimization algorithm enables quick convergence and captures complex temporal dependencies, ensuring early and accurate breast cancer prediction.