Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results

Jiangpeng Wu; Pengyi Zhang; Liting Zhang; Wenbo Meng; Junfeng Li; Chongxiang Tong; Yonghong Li; Jing Cai; Zengwei Yang; Jinhong Zhu; Meie Zhao; Huirong Huang; Xiaodong Xie; Shuyan Li

Random forest algorithm (RF) is a promising and well-known classifier with praiseworthy characteristics that can use multiple trees to train and predict samples, which has received extensive attention in the fields of chemometrics and bioinformatics. 23, 24 The algorithm in this study was used to establish the final assistant discrimination tool to proactively seek out patients with COVID-19. The whole process could be divided into three stepwise stages. In the first stage, all the parameters were fully employed to build the model in order to evaluate the importance of each parameter. In the second stage, while the top-ranking parameters were increased one by one, different models were built under the adjustment of RF parameters including the tree number (ntree) and the number of randomly selected features to split at each node (mtry). The adjustment range of ntree ranged from 500 to 1500 with a step of 100; Once the value of ntree was fixed, mtry spanned from 2 to 15 via a step of 1. In the last stage, based on the principle that the minimum number of top-ranking parameters could have comparable performance with the whole parameters, the appropriate number of parameters, ntree, and mtry were selected respectively. The study was executed on R package randomForest v4· 6-7.