一个新的流式K-最近邻近算法用于区块分散,自相关,不规则的纵向数据中的状态预测
Xin Zhao1, Xiaokai Nie2,3,4, Yu Zhao5
1School of Mathematics, Southeast University, Nanjing, People's Republic of China.
Statistics in medicine
|December 2, 2025
概括
这项研究引入了一种新的K-Nearest Neighbor (KNN) 算法,用于在复杂的纵向数据流中进行状态预测. 该方法有效地处理不平衡的类和不规则的数据,在模拟和现实世界的医疗数据集中实现高精度.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 由于区块分散,自相关和不规则的变量,在纵向数据流中难以预测状态.
- 现有的方法难以处理这些数据,特别是当类不平衡时.
研究的目的:
- 开发一个强大的K-Nearest Neighbor (KNN) 算法,用于在纵向数据流中进行状态预测.
- 为应对数据不规则性,稀疏性,自相关性和阶级不平衡所带来的挑战.
主要方法:
- 提出了一个K-近邻 (KNN) 算法,利用Kullback-Leibler (KL) 差异进行距离测量.
- 采用了米度条件密度的特征,有或没有第一阶段滞后.
- 开发了一种缺乏分析表达式的分布的数值方法,应用于现实世界的数据.
主要成果:
- 流式KNN算法在模拟的高斯分布和逆马分布上实现了接近1的曲线下面面积 (AUC).
- 应用于大型医疗流数据集的数值方法产生AUC为0.913,灵敏度为0.851,特异性为0.816.
结论:
- 拟议的流式KNN算法在复杂的纵向数据的状态预测方面表现出很高的性能.
- 该方法即使在高度不平衡的类和不规则的数据特征的情况下也有效.
- 这种方法对大数据医疗分析中的应用具有重大前景.
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