模糊DEMATEL推断系统用于准确和高效的心血管疾病诊断
Stephen Mariadoss1, Felix Augustin1
1Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Computer methods in biomechanics and biomedical engineering
|August 23, 2023
概括
这项研究提出了一种新的模糊系统,用于准确的心血管疾病自我检测. 新模型实现了高精度,改善了早期检测和预防策略.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管疾病研究研究
背景情况:
- 心血管疾病对全球健康构成重大威胁.
- 早期检测和预防对于控制心血管疾病至关重要.
- 现有的自我检测模型缺乏足够的准确性和全面性.
研究的目的:
- 引入一个新的模糊断决策试验和评估实验室 (DEMATEL) 推断系统,用于准确的心血管疾病自我检测.
- 通过改进的自我检测模型,增强心血管疾病的早期检测和预防.
- 通过优先考虑影响因素来解决心血管疾病诊断的复杂性.
主要方法:
- 模糊的DEMATEL,和Mamdani模糊推理的整合.
- 用属性减少和基于的聚类用于数据预处理的应用.
- 利用影响因素选择和规则减少来优化系统.
主要成果:
- 拟议的系统实现了98.69%的高精度.
- 该系统表现出高灵敏度,达到98.62%.
- 该模型在心血管疾病自我检测准确度方面超过了现有方法.
结论:
- 模糊DEMATEL推断系统为心血管疾病自我检测提供了一个高度准确和有效的方法.
- 该系统处理复杂性和优先考虑因素的能力使其成为早期检测的宝贵工具.
- 这种新型系统在改善心血管疾病预防和管理策略方面具有重大潜力.
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