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K-最近邻近算法用于赋值缺失的纵向产前酒精数据.

Ayesha Sania1,2, Nicolò Pini1,2, Morgan E Nelson3

  • 1Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.

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概括

该K近邻 (k-NN) 算法准确地归因孕妇缺少的酒精消费数据. 这种机器学习方法提高了纵向妊娠研究的数据完整性.

关键词:
数据归算数据的归算方法数据的缺失数据的缺失.k 最接近的邻居k-NNN 在线观看机器学习是机器学习.产前酒精数据 产前酒精数据

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科学领域:

  • 流行病学 流行病学
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 对孕妇的长度研究经常面临缺少酒精消费数据的挑战.
  • 不准确或不完整的数据可能会在产前酒精暴露研究中的结果中产生偏见.
  • 强大的归算方法对于可靠分析此类数据至关重要.

研究的目的:

  • 评估K最近邻居 (k-NN) 机器学习算法的有效性,以归因缺少的每日酒精消费数据.
  • 评估在大量孕妇前队列中k-NN归算的准确性 (安全通道研究).
  • 为了确定k-NN归算的最佳参数以尽量减少错误.

主要方法:

  • 使用了来自安全通道研究 (n=11,083) 的数据,缺少酒精消费数据 (11.4%).
  • 应用了k-NN算法,对距离进行加权,并根据周日进行匹配以进行归算.
  • 通过随机删除数据段并将归算与实际值进行比较,验证了归算准确性.

主要成果:

  • 使用5个最近邻居 (K=5) 和55天段的k-NN算法产生了最小的归算误差.
  • 导入值与删除部分的64%的实际值非常接近,并且在31%的删除部分中处于+/-1饮料/天范围内.
  • 计量准确度在研究地点之间显示出因饮酒模式和缺失数据比例的差异而存在变化.

结论:

  • 在K最接近邻居 (k-NN) 算法提供了一个非常准确的方法,在纵向怀孕研究中赋予缺少的酒精数据.
  • k-NN归算可以显著提高研究的数据质量和可靠性检查怀孕期间的酒精使用.
  • 这种机器学习方法为解决敏感公共卫生研究数据缺口提供了有价值的工具.