COVID-19 Diagnosis Prediction in Emergency Care Patients using the Convolutional Neural Network
Kemal Adem, Serhat Kılıçarslan
Abstract
The sudden increase in cases of Coronavirus disease (COVID-19) puts a high pressure on health care providers in many countries across the world. In the present case, an early and correct diagnosis of the disease, and starting the treatment is of vital importance. Most of the developing countries have insufficient RT-PCR tests, the most verified diagnostic test for COVID-19. This increases the number of infected patients and delays preventive measures. In this study, the risk of a positive COVID-19 diagnosis is estimated by applying Convolutional Neural Network (CNN) method, which is a deep learning model, to the dataset obtained from routine blood tests of all patients who admitted to the emergency service. The dataset used in the experiments consists of the data from patients admitted to the Israelita Albert Einstein Hospital in São Paulo, Brazil, between March 28th and April 3rd, 2020. In addition to the J48, ANN, Random Forest, and Random Committee data mining algorithms, the CNN deep learning algorithm were applied to the dataset. The 5 and 7 fold cross validation model was applied to the data set and the average of the two models was used as an evaluation criterion in order to ensure objectivity. The best prediction performance was obtained by the CNN method by 92.52% accuracy. Experimental results revealed that the proposed approach is in line with the results of the tests with general validity.