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使用混合支持向量回归模型与和黑猩猩优化器进行增强的诊断,可疑患有沙尔科病的患者

Guogang Xie¹, Hani Attar², Ayat Alrosan³

¹Department of Respiratory and Critical Care Medicine, Shanghai General Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.

PeerJ. Computer science

|February 3, 2025

概括

研究人员开发了一种新型的机器学习模型,以利用血清中血管酶转化酶 (ACE) 水平预测沙尔科毒症患者的可溶性互白素2受体 (sIL-2R) 水平. 这种SVR-BE-CO模型为治疗反应评估提供了更高的准确性.

科学领域:

肺部医学肺部医学
生物医学工程生物医学工程
机器学习机器学习

背景情况:

评估沙丘症治疗反应需要可靠的生物标志物.
可溶性互白素2受体 (sIL-2R) 是一种潜在的疾病活性指标.
预测sIL-2R水平可能有助于肺功能评估.

研究的目的:

开发一种机器学习模型,用于预测沙尔科病的sIL-2R水平.
为了利用血清 ангиотензин转化酶 (ACE) 水平作为预测的输入.
引入一种新的混合预测模型SVR-BE-CO,以提高准确性.

主要方法:

开发了一个支持向量回归 (SVR) 模型.
优化了SVR超参数,使用混合的白优化器 (BES) 和黑猩猩优化器 (CO) 模型 (BES-CO).
与其他预测方法 (SVR-FFA,DT,SVR-GWO,RF) 相比,评估了SVR-BE-CO模型.

主要成果:

该SVR-BE-CO模型在预测sIL-2R水平方面表现出卓越的准确性.
与SVR-FFA,DT,SVR-GWO和RF等既定方法相比,实现了更高的预测性能.
表明该模型的潜力可以作为对 Sarcoidosis 管理的可靠指标.

结论:

这种新型的SVR-BE-CO模型准确地预测了在肉类发病中的sIL-2R水平.
这种方法为监测治疗反应和肺功能提供了一个有希望的工具.
突出了混合优化算法在生物医学预测中的有效性.

关键词:

ангиотензин 转化酶转化酶头搜索搜索搜索头搜索搜索黑猩猩优化器是什么机器的引力机器的引力 Sarcoidosis 是一种癌症.可溶性IL-2受体可以溶解.

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