提高胃癌的早期检测:一个多层次优化的特征选择模型,具有交叉信息反
Jiejun Lin1, Fangchao Zhu1, Xiaoyu Dong2
1Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
Computers in biology and medicine
|May 7, 2024
概括
这项研究提出了一种新的方法,bCIFMVO-FKNN-FS,用于早期检测胃癌 (GC). 该模型在识别早期GC患者方面取得了很高的准确性,提高了诊断能力.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算医学是一种计算医学.
背景情况:
- 早期发现胃癌 (GC) 对于改善患者的生存率和治疗结果至关重要.
- 目前的诊断方法可能在识别疾病的初始阶段方面存在局限性.
- 开发先进的查工具对于及时干预至关重要.
研究的目的:
- 引入和验证基于包装的创新特征选择方法,bCIFMVO-FKNN-FS,用于早期GC检测.
- 提高识别早期GC患者的准确性和效率.
- 确定与早期GC相关的关键临床参数.
主要方法:
- 开发了一个新的bCIFMVO-FKNN-FS模型,将交叉信息反多节优化器 (CIFMVO) 与模糊的k-最近邻居 (FKNN) 集成在一起.
- 在IEEE CEC基准函数上对11个最先进的算法进行验证的CIFMVO性能.
- 将bCIFMVO-FKNN-FS模型应用于诊断为早期GC或慢性胃炎的1632名患者的临床数据.
主要成果:
- CIFMVO算法在各种维度中证明了竞争优化效率.
- 对于早期GC检测,bCIFMVO-FKNN-FS模型实现了83.395%的预测准确度和87.538%的灵敏度.
- 确定了年龄,性别,血清胃素-17,血清素I和PGI/PGII比率作为早期GC的重要指标.
结论:
- 拟议的bCIFMVO-FKNN-FS模型对于早期查胃癌是有效的.
- 鉴定的临床参数为早期GC诊断提供了有价值的见解.
- 这项研究有助于开发用于及时检测和管理GC的先进工具.
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