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

It was highly important to select appropriate number of parameters for the foundation of the final discrimination tool with the advantages of convenient operation and accurate diagnosis. Based on the minimum number of top-ranking parameters without compromising performance, 11 laboratory findings were carefully selected as the final input indicators as shown in Figure 1 . The final model revealed good prediction performance on the training set with ACC, MCC, and related AUC values of 0· 9795, 0· 9594, and 1· 000, respectively. These important metrics implied that the tool integrated with 11 top-ranking parameters had the significant ability to discriminate patients with COVID-19. At the same time, the improvement of sensitivity (0· 9750) and specificity (0· 9909) heralded that this method had great potential as a practical clinical tool for large-scale initial screening. Although 34 parameters seemed like a sensible choice, the redundant ones didn't play an irreplaceable role in sharply improving the performance, which might make the model over-fitting and prolong the diagnosis process. So the selected parameters were of extraordinary benefit to build a cost-effective and rapid assistant tool which had commensurate ability compared with the whole parameters.