机器和深度学习算法用于分类不同类型的痴呆症:文献综述
Masoud Noroozi1, Mohammadreza Gholami2, Hamidreza Sadeghsalehi3
1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Applied neuropsychology. Adult
|August 1, 2024
概括
机器学习 (ML) 和深度学习 (DL) 显示出早期痴呆症诊断的前景. 这些算法可以帮助识别像阿尔茨海默病这样的疾病,但需要进一步验证和道德考虑.
科学领域:
- 神经学 神经学
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 痴呆症是一种全球性的认知障碍,影响着数百万人. 早期识别和治疗对于管理诸如阿尔茨海默氏症,前,莱维体和血管痴呆症等疾病至关重要.
- 机器学习 (ML) 和深度学习 (DL) 为诊断和管理痴呆症提供了潜在的进步.
研究的目的:
- 审查有关应用ML算法的文献,以诊断各种类型的痴呆症.
- 为了比较不同的ML算法,讨论特征选择,数据准备以及它们在预测疾病进展和治疗反应中的作用.
主要方法:
- 对用于痴呆症诊断的ML算法进行文献综述.
- 算法的比较,包括支持向量机器,人工神经网络,决策树和随机森林.
- 讨论ML模型的特征选择,数据准备和预测能力.
主要成果:
- 机器学习算法显示了提高痴呆症诊断准确性的潜力,特别是在早期阶段.
- 不同的ML算法对痴呆症诊断有不同的好处和缺点.
- 仔细的特征选择和数据准备是开发准确的ML模型的关键.
结论:
- 机器学习算法可以帮助早期诊断和预测痴呆症的进展和治疗反应.
- 需要进一步的研究来验证ML和DL在临床环境中的有效性,并解决伦理方面的问题.
- 机器学习工具应该增强,而不是取代临床判断,以确定痴呆症诊断.
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