Covid-19 rapid test by combining a random forest based web system and blood tests

Abstract

The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already caused over 400 thousand deaths to date. The diagnosis of the disease has an important role in combating Covid-19. Proposed method In this work, we propose a web system, Heg.IA, which seeks to optimize the diagnosis of Covid-19 through the use of artificial intelligence. The main ideia is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. It will indicate if the patient is infected with SARS-Cov2 virus, and also predict the type of hospitalization (regular ward, semi-ICU, or ICU). We developed a web system called Heg.IA to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU. This application is based on decision trees in a Random Forest architecture with 90 trees. The system showed to be highly efficient, with great results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891% ± 0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. We also expect the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving lives.

SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples

“…In studies of [34,37,39] a similar approach has been proposed in terms of predicting hospitalization type, and their performance metrics were given in Table 4. In [34] empty values were filled with 0 and 41 exams have used for predicting whether the patient should be sent to RW, SICU, ICU, or no hospitalization. They reached the best score with RF.…”

“…In another study, Random Forest (RF) has been selected as the best method and a web-based system has been built with RF to the detection of Covid-19 and the severity level of the disease. 41 examinations included in the model and 92.81% accuracy has been obtained for predicting positive-negative cases while 99% accuracy has achieved as for the indication of hospitalization [34,35] [36]. In a very similar study that used the same hemogram data, 598 patients and 14 features were used for building a machine learning prediction model.…”

“…In this study, the same data set that was used in studies of [31][32][33][34][35][36][37][38][39][40] has been collected from, public database, Kaggle [41] to predict Covid-19 severity level. The preferred data set contains 111 blood analyses with a required hospitalization type of 5644 patients yet a huge part of the data contains missing values.…”

COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

“…Tang et al [19] built a RF model to predict the severity of a COVID-19 diagnosis from chest CT images. Barbosa et al [20] utilized a RF classifier to diagnose COVID-19 from blood samples. Gupta et al [21] predicted cases of COVID-19 in India with a RF model.…”