优化肺癌预测:利用Kernel PCA与树突神经模型进行PCA
Umair Arif1, Chunxia Zhang1, Muhammad Waqas Chaudhary1,2
1Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
概括
这项研究使用树突神经模型 (DNM) 与特征选择和提取相结合,增强了肺癌预测. 内核PCA (K-PCA) 集成显著提高了DNM的性能.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 延迟肺癌诊断导致高死亡率.
- 有效的预测模型对于早期检测和治疗优化至关重要.
- 传统的机器学习 (ML) 模型需要增强,以改善肺癌预测.
研究的目的:
- 为了增强一个树突神经模型 (DNM) 用于肺癌预测.
- 通过协作功能选择和提取来提高准确性,精度和灵敏度.
- 将增强的DNM与传统的ML模型进行比较.
主要方法:
- 利用了1000名肺癌患者的数据集,具有23个特征.
- 采用了协作特征选择和提取技术,包括主要组件分析 (PCA),内核PCA (K-PCA) 和统一多重近似和投影 (UMAP).
- 使用准确度,精度,F1得分,灵敏度,特异性和混矩阵评估模型性能.
主要成果:
- 与内核PCA (K-PCA) 集成的树突神经模型 (DNM) 实现了98.50%的准确性,99.42%的精度和98.84%的灵敏度.
- 主要组件分析 (PCA) 的结果为96.50%的准确性,96.64%的精度和97.45%的灵敏度.
- 统一多重近似和投影 (UMAP) 的准确率为98%,精度为98.82%,灵敏度为98.82%.
结论:
- 用Kernel PCA增强的树突神经模型 (DNM) 在肺癌预测方面表现出卓越的性能.
- 拟议的方法比传统的机器学习模型提供了显著的改进.
- 这种增强的DNM具有促进肺癌诊断和改善医疗保健研究患者结果的潜力.
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