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利用ANFIS与亚当和PSO优化器为帕金森病的优化器
Akram Pasha1, Syed Thouheed Ahmed2, Ranjith Kumar Painam3
1Department of Computer Science and Engineering, REVA University, Bengaluru, India.
Heliyon
|May 9, 2024
概括
这项研究引入了一种使用自适应神经模糊推理系统 (ANFIS) 与亚当和粒子群优化 (PSO) 的混合方法,用于准确的帕金森病.
科学领域:
- 计算神经科学是一种计算神经科学.
- 医学中的人工智能.
- 生物医学工程 生物医学工程
背景情况:
- 帕金森病 (PD) 是一种进展性神经退行性疾病,影响全球数百万人.
- 准确的PD早期诊断对于有效管理和减少残疾至关重要.
- 由于疾病的复杂性,传统的诊断方法面临挑战.
研究的目的:
- 开发和评估一种新的混合智能诊断模型,用于准确预测帕金森病.
- 为了比较适应性神经模糊推理系统 (ANFIS) 的性能,该系统与Adam和粒子优化 (PSO) 进行了优化.
主要方法:
- 开发了一种混合ANFIS模型,集成了Adam和PSO优化器.
- 模型参数,包括成员函数和时代,通过比较分析进行了优化.
- 使用临床和人口统计数据评估绩效,重点关注准确性,精度,回忆,f1分数和损失等指标.
主要成果:
- 在损失和精度方面,ANFIS (PSO) 显示出卓越的性能.
- ANFIS (Adam) 在准确性,f1得分和回忆方面取得了更好的结果.
- 这两种混合型号都显示出PD诊断的巨大潜力.
结论:
- 拟议的自适应神经模糊算法为准确诊断帕金森病提供了一个有希望的策略.
- 混合ANFIS模型非常适合用于PD检测和其他实际应用.
- 智能诊断系统可以克服传统临床试验的局限性.
关键词:
适应性神经模糊推理系统 (ANFIS)人工智能的人工智能是人工智能.分类 分类 分类 分类.临床决策支持 临床决策支持数据分析数据分析数据挖掘是一种数据挖掘.功能提取 功能提取功能选择 功能选择医疗保健信息技术 医疗保健信息技术机器学习是机器学习.神经网络的神经网络的神经网络帕金森病 (PD) 是一种疾病.粒子群集优化 (PSO) 是一种预测分析是一种预测分析.更多相关视频
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