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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
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使用支持向量回归模型提高骨年龄估计的准确性.

Ying Deng1, Xiaoyan Gao1, Taotao Tu2

  • 1Hubei University of Technology, National "111" Center for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), No.28, Nanli Road, Hongshan District, Wuhan, Hubei Province 430068, China.

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PubMed
概括
此摘要是机器生成的。

与传统方法相比,支持向量回归 (SVR) 模型显著提高了骨年龄估计的准确性. 这种机器学习方法提供了可靠的骨年龄评估,即使使用较小的数据集.

关键词:
对交叉验证进行验证.网格搜索搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格搜索网格骨年龄估计 骨年龄估计支持矢量回归的支持矢量回归

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科学领域:

  • 儿科放射学 儿科放射学
  • 医学成像分析分析 医学成像分析
  • 机器学习在医疗保健中的应用

背景情况:

  • 骨年龄估计对于评估儿童发育和诊断生长障碍至关重要.
  • 传统的骨年龄评估方法在准确性和一致性方面存在局限性.

研究的目的:

  • 评估支持向量回归 (SVR) 模型在提高骨年龄估计准确性的有效性.
  • 将基于SVR的指标与已建立的骨年龄评估指标的性能进行比较.

主要方法:

  • 利用来自中国武汉1至17岁的5018名个人数据集.
  • 采用交叉验证和网格搜索以获得最佳的SVR模型参数调整.
  • 将SVR衍生的骨年龄指标与原始TW3,CHN05和联合GP标准进行比较.

主要成果:

  • 与原始指标相比,SVR模型在骨年龄评估中表现出更高的可靠性和准确性.
  • 通过使用TW3,CHN05或组合数据集的SVR模型实现了一致的顶级预测准确性.
  • 模型性能在不同的训练集大小中保持稳健.

结论:

  • 支持向量回归为骨年龄估计的准确性和可靠性提供了显著的进步.
  • SVR模型显示了强大的骨年龄评估的潜力,在有限的数据集下特别有效.