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Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction.

Meilin Zhang1, Qianxi Wu1, Huiling Chen1

  • 1Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.

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Summary

This study introduces an improved machine learning model for early COVID-19 patient identification. The novel RRWOA algorithm enhances prediction accuracy, offering a valuable tool for clinical decision-making.

Keywords:
COVID-19Feature selectionRandom contraction strategyRosenbrock methodSwarm intelligenceWOAWhale optimization algorithm

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Area of Science:

  • Computational intelligence
  • Machine learning
  • Medical informatics

Background:

  • Coronavirus Disease 2019 (COVID-19) poses a significant global health challenge.
  • Early identification and intervention are crucial for managing critically ill patients.
  • Existing prediction models may require optimization for accuracy and efficiency.

Purpose of the Study:

  • To develop an efficient and intelligent prediction model for early identification of critically ill COVID-19 patients.
  • To propose an improved whale optimization algorithm (RRWOA) to enhance machine learning model performance.
  • To validate the efficacy of the proposed model using benchmark datasets and a real-world COVID-19 dataset.

Main Methods:

  • Development of the improved whale optimization algorithm (RRWOA) using random contraction strategy and Rosenbrock method.
  • Integration of RRWOA with the k-nearest neighbor (KNN) classifier for prediction.
  • Creation of a binary version (BRRWOA) for feature selection and evaluation on UCI datasets.
  • Comparative analysis of the proposed model against existing algorithms on a COVID-19 patient dataset.

Main Results:

  • RRWOA demonstrated superior performance over classical, advanced, and WOA variants on 30 IEEE CEC2014 functions, winning first place on multiple test functions.
  • BRRWOA achieved the smallest fitness value on eleven UCI datasets, proving its effectiveness in combinatorial optimization and discrete cases.
  • The proposed prediction model significantly outperformed five other algorithms on the COVID-19 dataset, indicating its clinical utility.

Conclusions:

  • The RRWOA algorithm is an effective optimizer, overcoming local optimum issues and showing strong performance in continuous and discrete optimization problems.
  • The developed machine learning model provides a robust framework for supporting clinical diagnostic decision-making in COVID-19 patient management.
  • This research contributes an intelligent system for early detection and intervention, potentially improving patient outcomes.