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Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM

Yu Zhu1, Mingxu Zhang2, Qinchuan Huang2

  • 1School of Sports Medicine and Health, Chengdu Sport University, Chengdu, 610041, China.

Scientific Reports
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced Secretary Bird Optimization Algorithm (QHSBOA) combined with Kernel Extreme Learning Machine (KELM) for accurate diabetes classification. The novel QHSBOA-KELM model demonstrates superior performance in early diabetes diagnosis and prediction.

Keywords:
Diabetes classification predictionKernel extreme learning machineParameter optimizationQuantum computingSecretary bird optimization algorithm

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Area of Science:

  • Public Health
  • Computational Biology
  • Machine Learning

Background:

  • Chronic disease classification is crucial in public health, with machine learning widely applied.
  • Diabetes is a prevalent chronic disease requiring robust classification methods.
  • Existing machine learning models for diabetes classification need further improvement.

Purpose of the Study:

  • To develop a novel and accurate diabetes classification prediction model.
  • To introduce an improved Secretary Bird Optimization Algorithm (QHSBOA) for enhanced classification.
  • To optimize Kernel Extreme Learning Machine (KELM) parameters using QHSBOA.

Main Methods:

  • Enhancement of the Secretary Bird Optimization Algorithm (SBOA) with particle swarm optimization, dynamic boundary adjustments, and quantum computing variations, termed QHSBOA.
  • Validation of QHSBOA performance using the CEC2017 benchmark suite.
  • Optimization of KELM kernel penalty parameter (γ) and bandwidth (σ²) using QHSBOA for diabetes classification.

Main Results:

  • The QHSBOA-KELM model demonstrated superior performance compared to other classification models.
  • The model achieved high accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity.
  • QHSBOA effectively optimized the KELM parameters for improved diabetes classification.

Conclusions:

  • The proposed QHSBOA-KELM approach provides an effective methodology for accurate diabetes classification.
  • This method offers potential for early diagnosis and prediction of diabetes.
  • The study highlights the efficacy of hybrid optimization algorithms in medical data analysis.