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Updated: May 30, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

基于量子计算和KELM糖尿病分类的多种策略改进的秘书鸟优化算法.

Yu Zhu¹, Mingxu Zhang², Qinchuan Huang²

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

Scientific reports

|January 30, 2025

概括

这项研究引入了一个增强的Secretary Bird优化算法 (QHSBOA) 与内核极端学习机器 (KELM) 结合,用于准确的糖尿病分类. 新的QHSBOA-KELM模型在早期糖尿病诊断和预测方面表现出卓越的表现.

关键词:

糖尿病分类预测和预测核心极端学习机器的核心.参数优化参数优化量子计算是一种量子计算.秘书鸟优化算法优化算法

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Last Updated: May 30, 2025

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科学领域:

公共卫生公共卫生
计算生物学计算生物学
机器学习机器学习

背景情况:

慢性疾病的分类在公共卫生中至关重要,机器学习被广泛应用.
糖尿病是一种常见的慢性疾病,需要强大的分类方法.
现有的用于糖尿病分类的机器学习模型需要进一步改进.

研究的目的:

开发一种新的,准确的糖尿病分类预测模型.
引入一个改进的秘书鸟优化算法 (QHSBOA) 进行增强的分类.
使用QHSBOA优化内核极端学习机器 (KELM) 参数.

主要方法:

加强秘书鸟优化算法 (SBOA) 的粒子群优化,动态边界调整和量子计算变化,称为QHSBOA.
使用CEC2017基准套件验证QHSBOA绩效.
优化KELM内核惩罚参数 (γ) 和带宽 (σ2) 使用QHSBOA进行糖尿病分类.

主要成果:

与其他分类模型相比,QHSBOA-KELM模型表现出卓越的性能.
该模型实现了高精度 (ACC),马修斯相关系数 (MCC),灵敏度和特异性.
QHSBOA有效地优化了KELM参数,以改善糖尿病分类.

结论:

拟议的QHSBOA-KELM方法为准确的糖尿病分类提供了有效的方法.
这种方法为早期诊断和预测糖尿病提供了潜力.
该研究强调了混合优化算法在医学数据分析中的有效性.