一种混合机器学习特征选择模型-HMLFSM,用于增强对多种结肠癌数据集应用的基因分类
Murad Al-Rajab1,2, Joan Lu2, Qiang Xu2
1College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
PloS one
|November 2, 2023
概括
这项研究引入了一种混合机器学习模型,用于改进结肠癌基因分类. 这种新的方法通过有效地选择相关的遗传特征来提高检测结肠癌的准确性.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 结肠癌是全球主要的健康问题,需要早期检测才能改善结果.
- 传统的结肠癌查方法,如结肠镜检查,是侵入性的.
- 机器学习提供了使用遗传数据进行结肠癌分类的非侵入性方法.
研究的目的:
- 开发一种改进的机器学习模型,用于非侵入性结肠癌基因分类.
- 解决传统机器学习模型在处理高维基遗传数据和可变基因表达方面的局限性.
- 通过有效的特征选择,提高结肠癌检测的准确性.
主要方法:
- 为结肠癌基因分类提出了一种混合特征选择模型 (HMLFSM).
- 实施了两阶段的特征选择方法,将信息获取 (IG) 与遗传算法 (GA) 结合起来,以及最小冗余最大相关性 (mRMR) 与粒子优化 (PSO).
- 在三个不同的结肠癌遗传数据集上测试了该模型.
主要成果:
- HMLFSM模型在结肠癌数据集上取得了显著的准确性改进,达到~95%,~97%和~94%.
- 该模型有效地识别了重要的和相关的基因,同时消除了不相关的基因.
- 证明选择性输入特征提取对于提高结肠癌基因分析的预测性能至关重要.
结论:
- 拟议的HMLFSM模型显著提高了结肠癌基因分类的准确性.
- 混合特征选择策略有效地解决了高维基遗传数据的挑战.
- 这种方法对更准确和非侵入性结肠癌检测有希望.
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