基于Levy飞行和自然仿生技术的神经网络预测模型,用于癌症预测中的应用
1Department of Oncology, Zigong Fourth People's Hospital, Zigong, China.
PloS one
|June 25, 2025
概括
这项研究介绍了LGWO-BP,这是一种结合灰狼优化器和雷维飞行的新方法,用于增强逆向传播神经网络,以准确预测癌症. 该方法在预测患者结果方面显示出更高的准确性和可靠性.
科学领域:
- 计算智能是一种计算智能.
- 生物医学信息学是生物医学信息学.
- 机器学习用于医疗保健
背景情况:
- 准确的癌症预测对患者护理和治疗规划至关重要.
- 当前的方法在实现高精度和可靠性方面面临挑战.
- 机器学习模型,特别是反向传播神经网络,广泛使用,但需要优化.
研究的目的:
- 开发一种先进的机器学习模型,用于精确的癌症预后.
- 提高癌症结局预测的准确性和可靠性.
- 为了优化反向传播神经网络,使用混合灰狼优化器和利维飞行方法.
主要方法:
- 调整灰狼优化器 (GWO) 与Levy飞行,以优化反向传播 (BP) 神经网络重量和偏差.
- 开发一种名为LGWO-BP的新方法用于癌症预后.
- 在各种公共数据集上对现有方法进行比较分析,包括癌症基因组图谱 (TCGA).
主要成果:
- 与GWO-BP相比,LGWO-BP方法的准确性和可靠性更高.
- 在多个数据集中,LGWO-BP实现了与最先进的方法相比具有竞争力的结果.
- 对于TCGA数据,LGWO-BP实现了0.70 (miRNA),0.72 (基因表达) 和0.72 (DNA甲基化) 的AUC得分,具有显著的精度和回忆值.
结论:
- 拟议的LGWO-BP方法显著提高了癌症预后的准确性.
- 这种方法为临床医生提供了更可靠的工具来预测患者的结果.
- 通过先进的机器学习应用程序,LGWO-BP有可能改善患者护理和治疗策略.
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