Comparing different machine learning techniques for predicting COVID-19 severity

作     者：Yibai Xiong Yan Ma Lianguo Ruan Dan Li Cheng Lu Luqi Huang the National Traditional Chinese Medicine Medical Team 

作者机构：Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesNo.16Nanxiao StreetDongzhimenDongcheng DistrictBeijing100700BeijingChina 不详 National Resource Center for Chinese Materia MedicaChina Academy of Chinese Medical SciencesNo.16Nanxiao StreetDongzhimenDongcheng DistrictBeijing100700BeijingChina 

出 版 物：《Infectious Diseases of Poverty》 (贫困所致传染病（英文）)

年 卷 期：2022年第11卷第1期

页      面：97-97页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：National Administration of Traditional Chinese Medicine, (ZYYCXTD-D-202005) Centers for Disease Control and Prevention, CDC 

主　　题：COVID-19 Severity Machine learning Support vector machine Random Forest Logistic regression 

摘      要：Background Coronavirus disease 2019(COVID-19)is still ongoing spreading globally,machine learning techniques were used in disease diagnosis and to predict treatment outcomes,which showed favorable *** present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest(RF),support vector machine(SVM),and logistic regression(LR).Feature importance to COVID-19 severity were further *** A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28,2020,eighty-six demographic,clinical,and laboratory features were selected with LassoCV method,Spearman’s rank correlation,experts’opinions,and literature ***,SVM,and LR were performed to predict severe COVID-19,the performance of the models was compared by the area under curve(AUC).Additionally,feature importance to COVID-19 severity were analyzed by the best performance *** A total of 287 patients were enrolled with 36.6%severe cases and 63.4%non-severe *** median age was 60.0 years(interquartile range:49.0–68.0 years).Three models were established using 23 features including 1 clinical,1 chest computed tomography(CT)and 21 laboratory *** three models,RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928,RF also achieved a favorable sensitivity of 96.7%,specificity of 69.5%,and accuracy of 84.5%.SVM had sensitivity of 93.9%,specificity of 79.0%,and accuracy of 88.5%.LR also achieved a favorable sensitivity of 92.3%,specificity of 72.3%,and accuracy of 85.2%.Additionally,chest-CT had highest importance to illness severity,and the following features were neutrophil to lymphocyte ratio,lactate dehydrogenase,and D-dimer,*** Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19,which may facilitate effective care and further optimize resources.