Online disease risk monitoring using DEWMA control chart

Abstract

Early detection of a disease risk plays a vital role in successful treatment of the disease. Many chronic diseases, e.g., stroke, can be treated satisfactorily if they can be detected early. Traditionally, people evaluate their health conditions by comparing the current readings of their medical risk factors with some threshold values, and if irregular readings are triggered out, further medical tests are conducted to find the causes of the disease. A limitation of the traditional disease detection methods is the usage of only current time point data while ignoring the historical data. To use the history of the process for early detection of the disease using statistical process control, we suggest the use of a double exponential weighted moving average control chart to monitor risk factors sequentially. For the estimation of disease risk factors, different kernel functions are used. In particular, we use Epanechnikov, triangular, tricube, biweight, Gaussian, triweight, and cosine kernels. To evaluate the performance of different kernel functions, we conducted simulations as well as a real data set is used. The real data set is about 1055 stroke patients, out of which 27 individuals have suffered from a stroke attack at least once during the study time and the remaining 1028 people did not experience stroke. Numerical results show that the suggested method performs well in detection of early disease risk. Furthermore, it is observed that the biweight kernel function performs better than the other kernel functions for online disease risk monitoring. It is also noticed that for small smoothing parameters of the DEWMA chart, the Epanechinkov and cosine kernels perform better whereas tricube and biweight for the large smoothing parameters.