The lasso binary logistic regression method for selecting variables that affect the recovery of Covid-19 patients in China

Abstract

The Lasso regression performs the least squares method with the l 1-constraint. This is a particular type of regularization which adds a penalty equal to the absolute value of the magnitude of coefficients that can result in sparse models with few coefficients in which some coefficients can become zero and be eliminated. Larger penalties result in coefficient values closer to zero, which is ideal for producing simpler models. In this study, the lasso method has been applied to select variables affecting the recovery of the Covid-19 patients. The data consisted of the number of patients treated in several hospitals or clinics in China, recovered patients, demographics, comorbidities, symptoms, and treatment. The performance of the lasso binary logistic regression was compared with the full model of logistic and the stepwise logistic regression. The results showed that the number of independent variables selected by the lasso method was larger than those selected by the stepwise method. It has also been showed that coefficient values of variables produced by the lasso method were smaller than those produced by the stepwise. The independent variables that affect the cure rate of covid-19 patients with the lasso are gender, comorbidities, diabetes, cardiovascular, cough, fatigue, diarrhea, platelet count and antibiotics.