体检指标健康预警的灰色-时序组合模型

对于个体健康体检数据而言，传统的以大样本为基础的数学模型无法满足体检数据的建模需求。基于个体体检数据特征分析，首先构建适用于个体体检指标健康预警的近似非齐次指数序列的改进离散灰色模型。其次，为降低单个模型预测精度的有限性，利用方差倒数法为离散灰色模型和差分自回归移动平均模型赋权重，在模型误差平方和达到最小时取得最佳的权重值。从而将两个模型的预测结果进行组合，实现对健康指标的建模与趋势分析，及时掌握个体健康指标的变化并发现潜在的疾病隐患。预测模型在实验数据集上的相对模拟误差与最优基准模型相比有所下降，表明灰色–时序组合模型具有更高的模拟精度，解决了传统的依据单次体检指标进行静态分析的弊端以及单个模型预测结果的局限性，更加关注个体差异，能有效提升健康预警的效果。

Grey time series combination model for health warning of physical examination indexes

For individual health examination data, the traditional mathematical model based on large samples can not meet the modeling requirements of physical examination data. Based on the analysis of the characteristics of individual physical examination data, an improved discrete grey model of approximately non-homogeneous index series suitable for individual physical examination indicator health warning was first constructed. Secondly, in order to reduce the limitation of the prediction accuracy of a single model, the inverse variance method was used to assign weights to the discrete grey model and the differential autoregressive moving average model, and the best weight value was obtained when the sum of squares of the model errors reached the minimum. Thus, the prediction results of the two models were combined to achieve the modeling and trend analysis of health indicators, timely grasp the changes of individual health indicators and discover potential disease hazards. The relative simulation error of the prediction model on the experimental data set decreases in comparison with the optimal benchmark model, which indicates that the grey time series combination model has higher simulation accuracy. The shortcomings of traditional static analysis based on single physical examination indicators and the limitations of single model prediction results are solved. Individual differences are emphasized, and the effect of health warning can be effectively improved.