增强的Runge-Kutta驱动的特征选择模型,用于早期检测胃食道逆流病
Jinlei Mao1, Zhihao Zhu1, Minjun Xia1
1General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
Computers in biology and medicine
|April 24, 2024
概括
这项研究引入了一种新的bSCCRUN-FKNN-FS模型,用于准确预测胃食道逆流性疾病 (GERD). 该模型实现了高精度,有助于早期诊断和预防严重并发症.
科学领域:
- 医疗信息学 医疗信息学
- 计算智能是一种计算智能.
- 胃肠病学 胃肠病学
背景情况:
- 胃食道逆流性疾病 (GERD) 显著影响生活质量,并可能导致严重的并发症,如食道癌.
- 早期诊断GERD对于预防疾病进展和不良结果至关重要.
研究的目的:
- 开发和评估一种基于封装的新型特征选择模型,bSCCRUN-FKNN-FS,以改进GERD预测.
- 增强Runge Kutta算法 (RUN) 的性能,以在预测模型中获得更好的收精度.
主要方法:
- 引入了一个增强的Runge Kutta算法 (SCCRUN),结合了螺旋式通信和协作 (SCC),以改善本地搜索和融合.
- 开发了bSCCRUN-FKNN-FS模型,通过将SCCRUN与模糊的k-最近邻居 (FKNN) 集成,用于特征选择和GERD分类.
- 在IEEE CEC 2017基准上验证了SCCRUN,并使用179个GERD病例 (2019-2023) 的临床数据集评估了bSCCRUN-FKNN-FS.
主要成果:
- 与现有方法相比,SCCRUN算法展示了改进的优化能力.
- 该bSCCRUN-FKNN-FS模型实现了高预测准确度为93.824%的GERD.
- 确定了关键的预测特征,包括食道间隙直径 (扩张和非扩张) 和食道胃结直径.
结论:
- 拟议的bSCCRUN-FKNN-FS模型显示了对准确和早期识别GERD患者的巨大潜力.
- 增强的SCCRUN算法在与医学预测相关的优化任务中提供了卓越的性能.
- 使用先进的计算模型准确预测GERD可以帮助及时干预和管理.
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