Abstract: Aiming at the problems that it is difficult to predict the future health situation through the finite health data with unequal time interval sampling and the accuracy of the traditional non-equidistant grey prediction model is low in the short-term health prediction, an improved non-equidistant grey Markov prediction model is proposed. Firstly, the improved model reduces the impact of data mutation on the prediction results through data preprocessing and optimization of the prediction process. Secondly, the optimal weight coefficient is designed to optimize the model construction. Finally, the residual error is corrected by the strategy of grey and Markov correction. After the comparative analysis on actual monitoring data, the results show that the proposed model has higher prediction accuracy, so that the short-term health situation can be predicted relatively accurately.

Key words: health prediction, grey theory, non-equidistant, weight coefficient, Markov model