使用基于自适应网络的模糊推理系统来预测成功的衰老:与常见的机器学习算法进行比较
Azita Yazdani1,2,3, Mostafa Shanbehzadeh4, Hadi Kazemi-Arpanahi5
1Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
BMC medical informatics and decision making
|October 20, 2023
概括
适应性神经模糊推理系统 (ANFIS) 准确地预测了老年人的成功衰老 (SA). 这种智能模型为医疗保健提供了可靠的工具,在预测准确性方面超过了其他机器学习方法.
科学领域:
- 老年学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 全球人口正在老龄化,因此对成功老龄化 (SA) 的有效策略的需求越来越大.
- SA是一个复杂的,主观的概念,在定义和测量方面存在挑战.
- 开发预测模型可以帮助理解和促进SA.
研究的目的:
- 为成功的衰老 (SA) 提出一个智能预测模型.
- 评估拟议模型的性能与已建立的机器学习算法对比.
主要方法:
- 一项回顾性研究利用了784名老年人的数据.
- 一个自适应的神经模糊推断系统 (ANFIS) 已开发来预测SA.
- ANFIS的性能与多层感知子 (MLP),支向量机 (SVM) 和随机森林 (RF) 进行了比较,使用准确度,灵敏度,精度和F-score.
主要成果:
- 使用gauss2mf会员函数的ANFIS模型实现了卓越的预测性能.
- 安菲斯显示了高精度 (91.57%),灵敏度 (95.18%),精度 (92.31%) 和F分数 (92.94%).
结论:
- 在预测成功的衰老方面,ANFIS显著优于其他机器学习模型.
- 开发的ANFIS模型可以成为决策支持系统的基础,以提高老年人护理成果.
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