Application of Improved Grey Model GM(1,1) in Prediction of Human Health Data

Blood pressure is an important reflection of human health. With the introduction of precision medicine in recent years, due to the individual differences, the range of hypertension should not be used as a test standard for a set of data. But it should be determined by the individual's daily blood pressure values. This paper chooses the gray model, which is suitable for small-sample data prediction, complete a short-term prediction of blood pressure to determine the individual's fluctuations in blood pressure data. However, the standard gray model GM (1,1) doesn't satisfy. Firstly, coordination conditions, the background and gray values don't have the consistency of the transformation. Secondly, when the cumulative model is generated, the first point of the original sequence doesn't play a role in enhancing the accuracy of predictions; resulting in inaccurate prediction results. Therefore, we combine the improved gray model GM(1,1) with an equal-dimension information substitution model to predict the individuals blood pressure. The experimental results indicate that this model improves the accuracy of blood pressure prediction and it could prevent while treating early hypertension or other diseases, meeting the actual needs of doctors and patients.