帕金森病的机器学习:对数据集,算法和挑战的全面审查
Sahar Shokrpour1, AmirMehdi MoghadamFarid2, Sepideh Bazzaz Abkenar3
1Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran.
NPJ Parkinson's disease
|July 2, 2025
概括
机器学习 (ML) 通过分析各种数据来帮助诊断帕金森病 (PD). 这次审查强调了ML的ML.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 帕金森病 (PD) 显著影响老年人的健康,影响移动性和认知能力.
- 目前的治疗方法有限,需要先进的诊断工具.
- 机器学习 (ML) 提供了提高PD检测准确性的潜力.
研究的目的:
- 系统地审查ML在帕金森病诊断中的应用.
- 识别PD诊断中使用的数据集,算法和评估指标.
- 讨论基于ML的PD检测方面的挑战和未来方向.
主要方法:
- 133篇论文 (2021年至2024年4月) 的系统文献综述 (SLR).
- 研究被分为五个数据类别:声学,生物标志物,医学成像,运动和多式联络.
- 对ML算法,数据集,优点,局限性和评估因素的分析.
主要成果:
- ML有效地利用声学,生物标志物,成像,运动和多式联络数据进行PD诊断.
- 不同的ML算法显示出承诺,性能因数据集类型而异.
- 在审查的研究中确定了共同的数据集和评估指标.
结论:
- ML显示出显著的潜力,以提高帕金森病诊断的准确性和效率.
- 需要进一步的研究来解决局限性,并优化临床应用的ML模型.
- 多模式数据集成和先进的机器学习技术是有希望的未来方向.
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