传统数学模型和机器学习模型的比较,基于最近数学模型预测糖尿病病的进展
Yingda Sheng1,2, Caimei Zhang1,2, Jing Huang1,2
1Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Digital health
|March 11, 2024
概括
数学模型有助于诊断糖尿病病等疾病. 本文将传统和机器学习模型进行比较,强调它们在大数据应用中的优缺点.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 糖尿病病的诊断往往需要侵入性手术.
- 数学模型提供了非侵入性诊断和预测潜力.
- 大数据时代呈现出多样化的数学建模方法.
研究的目的:
- 为了比较传统的数学模型和机器学习模型.
- 阐明每个建模类型的优缺点.
- 确定疾病建模的未来研究方向.
主要方法:
- 描述和比较传统的数学模型.
- 机器学习模型的描述和比较.
- 分析模型在疾病预测中的适用性.
主要成果:
- 传统模型提供可解释性,但可能需要广泛的领域专业知识.
- 机器学习模型擅长在大数据集中复杂的模式识别.
- 这里提供了一个全面的比较,强调了这两种方法之间的权衡.
结论:
- 传统和机器学习模型在疾病建模方面都有明显的优势.
- 需要进一步的研究来优化混合方法和验证模型.
- 增强的比较研究对于推进预测诊断至关重要.
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