新型统计学上相当的基于签名的混合特征选择和集体深度学习LSTM和GRU用于慢性病分类
Yogesh N1,2, Purohit Shrinivasacharya1,2, Nagaraj Naik3
1Siddaganga Institute of Technology, Tumkuru, Karanataka, India.
PeerJ. Computer science
|December 16, 2024
概括
这项研究引入了一种混合特征选择方法,将LASSO和SES结合起来,用于慢性病 (CKD) 识别. 这种方法提高了深度学习模型的准确性,以便更好地对CKD进行分类.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 的分类是复杂的,因为有许多变量.
- 传统的特征选择方法在高维和相关数据方面扎.
- 确定关键特征和最佳分类器仍然是CKD预测的挑战.
研究的目的:
- 为CKD识别提出一种新的混合特征选择方法.
- 开发一个集体深度学习模型,以改进CKD分类.
- 根据现有分类器对拟议方法的性能进行评估.
主要方法:
- 使用统计等价签名 (SES) 方法进行特征识别.
- 集成了最小绝对收缩和选择运营商 (LASSO) 与SES用于混合功能选择.
- 开发了一个集体深度学习模型,使用长短期记忆 (LSTM) 和封闭循环单元 (GRU) 网络.
- 使用准确度,精度,回忆和F1分数来评估模型性能.
主要成果:
- 混合特征选择方法与LSTM-GRU组合模型相结合,在分类准确度方面实现了2%的改进.
- 性能与决策树 (DT),随机森林 (RF),后勤回归 (LR) 和支持矢量机器 (SVM) 等个别分类器进行了基准测试.
- 识别了特定特征 (HEMO,POT,细菌,冠状动脉疾病),对分类的贡献很小.
结论:
- 拟议的混合特征选择和集体深度学习方法显著提高了CKD分类的准确性.
- 该方法为在高维医学数据集中进行特征选择和分类提供了强大的解决方案.
- 未来的工作可能涉及动态特征选择和结合临床知识以进一步增强.
相关概念视频
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