Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study

Zhao, Chun-Hong; Wu, Hui-Tao; Che, He-Bin; Song, Ya-Nan; Zhao, Yu-Zhuo; Li, Kai-Yuan; Xiao, Hong-Ju; Zhai, Yong-Zhi; Liu, Xin; Lu, Hong-Xi; Li, Tan-Shi

Data cleaning was made on the data obtained according to the inclusion and exclusion criteria. The variables that were excessively missing were removed. The individuals whose data showed obvious errors or missing situation were also removed. A baseline description analysis of the remaining variables was performed, where the normal distribution variables are expressed as mean ± standard deviation; the non-normal distribution variables are expressed using the median (interquartile range). A differential hypothesis test was conducted on the variables. The t-test was used for the numerical variables that did not violate normality test and homogeneity test of variance, while the remaining numerical variables were tested by the Mann-Whitney U test. With a = 0.05 as the test level, the differences of the interested variables were discussed.