一种混合红鹿和粗略设置的临床信息检索系统用于乙型肝炎诊断
Madhusmita Mishra1, D P Acharjya2
1Vellore Institute of Technology, School of Computer Science and Engineering, Vellore, 632014, India.
Scientific reports
|February 15, 2024
概括
结合粗略集合理论和红鹿算法的新混合方法提高了疾病预测的准确性. 这种方法有效地处理医疗保健数据中的不确定性,优于现有的决策树和粗略设置模型.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健决策需要强大的知识获取和不确定性管理.
- 传统方法在临床信息系统中与数据复杂性和不确定性作斗争.
- 群体优化对于特征选择是有用的,但在处理不确定性方面有限.
研究的目的:
- 开发一种混合模型,整合粗略的集合理论和红鹿算法,以加强医疗保健中的知识发现.
- 为应对临床决策支持数据分析中的不确定性挑战.
- 提高疾病识别和预测的准确性.
主要方法:
- 开发了一种混合策略,结合了粗略的集合理论和红鹿优化算法.
- 红鹿算法用于基于粗略设置依赖的最佳特征选择.
- 粗略的集合理论被用来生成决策规则.
主要成果:
- 拟议的混合模型在肝炎疾病预测中实现了91.7%的准确性.
- 这种准确性超过了决策树算法 (82.9%) 和传统的粗略集 (88.9%).
- 经验研究证明了该模型在管理不确定性方面的可行性.
结论:
- 粗略设置和红鹿算法的混合为医疗数据分析提供了一种可行和准确的方法.
- 这种方法有效地管理不确定性,并加强临床环境中的决策.
- 拟议的模型显示了改善疾病预测准确性的巨大潜力.
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