为检测和诊断非传染性疾病而进行混合的元启发式优化
Saleem Malik1, S Gopal Krishna Patro2, Chandrakanta Mahanty3
1Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India. baronsaleem@gmail.com.
Scientific reports
|March 6, 2025
概括
本研究介绍了用于早期检测非传染性疾病 (NCD) 的先进数据挖掘和优化算法. 新的方法提高了特征选择和分类准确性,提高了患者护理和临床预测.
科学领域:
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
- 在医疗保健中的数据科学.
背景情况:
- 医疗保健在早期非传染性疾病 (NCD) 检测和管理方面面临挑战.
- 随着COVID-19的爆发,人们越来越需要先进的NCD预测和治疗工具,特别是在高危人群中.
研究的目的:
- 为NCDs提出一个综合数据挖掘,特征选择和元启发性优化综合的综合框架.
- 开发和评估新的混合算法,以高效准确的NCD分类.
主要方法:
- 为了最小的特征集识别,开发了层次基因多重减少选择算法 (H-GMRA).
- 根据NCD特征选择 (CPSO-RST-NFS) 的粗略集合理论实现定制的基于函数的粒子群优化.
- 在各种NCD数据集上进行了广泛的实验,以验证框架的性能.
主要成果:
- 在识别具有高依赖比率的特征子集方面,H-GMRA表现优于传统方法.
- 通过将元启发式优化与特征选择相结合,CPSO-RST-NFS实现了提高效率和准确性.
- 该框架成功地选择了信息特征,并在各种NCD数据集中提高了分类准确性.
结论:
- 拟议的框架大大促进了非传染性疾病的早期检测和管理.
- 这项研究弥合了计算效率和疾病分类准确性之间的差距,提供了更好的患者结果.
- 这些发现为NCD研究和临床预测中的医疗保健从业者和数据分析师提供了宝贵的见解.
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