一种有效的面向角色的二元Walrus Grey Wolf方法用于早期慢性病检测的特征选择
1Department of CSE, PDA College of Engineering Gulbarga, Kalaburagi, Karnataka, 585105, India. mamatha.789@gmail.com.
International urology and nephrology
|May 15, 2024
概括
这项研究引入了一种新的深度学习方法,用于准确的慢性病分类. 该方法提高了诊断精度,改善了慢性疾病管理中的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 生物医学数据分析
背景情况:
- 慢性病 (CKD) 的临床决策面临的挑战是由于高度的变化,导致不确定性和不良结果.
- 由于依赖有限的生物属性,传统的CKD识别方法往往缺乏准确性.
- 深度学习为改善CKD检测和临床决策支持提供了潜力.
研究的目的:
- 提出一种精确的慢性病分类方法,使用深辐射偏差网络和puma优化算法的组合.
- 为了提高CKD分类的准确性,超越传统方法.
主要方法:
- 数据预处理涉及到光谱Z分数袋提升K-MeansSMOTE转换,包括缩放,清洁,平衡,编码和标准化.
- 功能选择使用了混合面向角色的二进制Walrus灰狼算法.
- 使用自动编码器与基于补丁的主要组件分析进行了尺寸缩小.
- 通过使用由puma算法优化的深辐射偏差网络来实现分类.
主要成果:
- 提出的方法在CKD数据集的风险因素预测上达到99.02%,在CKD数据集上达到99.15%的高准确度.
- 实验结果表明,与现有的CKD识别方法相比,其准确度更高.
结论:
- 结合的深辐射偏差网络和puma优化算法为慢性病的分类提供了高度准确的方法.
- 这种先进的技术有望改善CKD的早期检测和管理.
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