一种算法多属性决策方法用于心脏问题分析在中性学超软专家集下,具有模糊的参数化基于度的设置
Muhammad Ihsan1, Muhammad Saeed1, Agaeb Mahal Alanzi2
1Department of Mathematics, University of Management & Technology, Lahore, Pakistan.
PeerJ. Computer science
|December 11, 2023
概括
这项研究引入了一个模糊的参数化的中性质超软专家集 (FpNHse-set) 以改善医疗诊断. 这种新的方法提高了复杂,不确定的医疗数据的决策能力,特别是用于心脏病检测.
科学领域:
- 数学 数学 是一个数学.
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 模糊的参数化结构为处理不确定性提供了先进的工具.
- 现有的中性学集合扩展缺乏复杂数据分类所需的多参数近似.
- 模糊参数化的中性质超软专家集 (FpNHse-set) 通过将特征分类为子特征集来扩展这些功能.
研究的目的:
- 引入和利用模糊参数化的中性学超软专家集 (FpNHse-set) 用于医学诊断.
- 通过使用FpNHse-sets来适应桑切斯的方法,以实现更具适应性和可靠性的决策过程.
- 通过使用现实世界的数据来评估这种综合方法在诊断心脏病方面的有效性.
主要方法:
- 开发具有多参数近似函数的模糊参数化的中性质超软专家集 (FpNHse-set).
- 修改和应用的桑切斯的方法与FpNHse-sets集成用于医学诊断.
- 使用克利夫兰心脏病数据集的实施和验证.
主要成果:
- FpNHse-set有效地对复杂的数据进行分类,在不确定的环境中增强决策.
- 修改后的桑切斯方法,结合FpNHse-sets,显示了对心脏病诊断的有希望的结果.
- 克利夫兰数据集的经验验证证明了拟议方法的真实性和潜在益处.
结论:
- 整合FpNHse-sets和修改的桑切斯方法为医学诊断提供了一个强大的框架,特别是在心脏病方面.
- 这种方法提高了在涉及不确定的医疗数据的决策过程中的适应性和可靠性.
- 该研究强调了先进的数学结构在提高诊断准确性和医疗保健结果方面的潜力.
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