Research on the diagnosis of corona virus disease 2019(COVID-19) based on machine-learning

Abstract

In recent years, the Corona Virus Disease 2019(COVID-19) has brought a huge impact on people's daily life and the normal operation of society, and as machine learning research deepens, this technology could help detect viruses such as the novel coronavirus. According to this, how to accurately, quickly and effectively analyze and classify laboratory results with multiple indicators through the method of machine learning is the object of this paper. In order to explore the classification performance of various machine learning classification algorithms on laboratory results, linear and non-linear methods were used respectively to analyze and classify the laboratory results. In linear analysis, distance discriminant and fisher discriminant were used to explore the classification effect of linear classification on laboratory results. The non-linear analysis mainly used Adaptive Boosting(AdaBoost) and Random Forest algorithm which are widely used to test the classification effect under the influence of multiple indexes. In this paper, python and other tools were used to classify samples of different combinations by using the idea of cross validation. By comparing the running time and detection accuracy, it was found that Adaboost algorithm is applicable in most cases and was a relatively fast and accurate classification method. In addition, Random Forest algorithm had similar accuracy, but it might have better performance on a large data set.