eXtreme Gradient Boosting-based method to classify patients with COVID-19

Abstract

Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.

N-acetylcysteine Reduces Inflammasome Activation Induced by SARS-CoV-2 Proteins In Vitro

“…The clinical data of patients included in this work showed an increased age, 28-day mortality, hypertension, and serum inflammatory parameters such as CRP and LDH in the B.1.1.7 VOC patients compared with B.1 variant. All of these clinical parameters haven been previously correlated with high mortality [ 33 ] in COVID-19 patients. In this line, the expression of inflammasome components, as well as the activation of inflammasome measured as NLRP3/ASC punctate structures in tracheal tissue, was more elevated in B.1.1.7 VOC variant than in B.1 variant and correlated with the CRP and LDH serum values which indicate a more severe inflammation which could be driven by inflammasome activation.…”

From Data to Insight: Predicting Interstitial Glucose in Healthy Cohort with Non-invasive Sensor Technology and Machine Learning

“…1(b) demonstrate the testing of different input cases, including combinations and single sensor modalities, using two types of models: tree-based 23 and gradient boosting (GB)-tree-based approaches 24,25 , with 32-fold crossvalidation. These models are widely used and effective in various healthcare research studies 20,[26][27][28][29] . The evaluation metric R-Squared (R 2 ) was utilized to measure the correlation distribution between the sensor modalities and glucose data 30 .…”

Clinical predictions of COVID-19 patients using deep stacking neural networks

“…Burdick et al 18 used an extreme gradient boosting (XGBoost) classifier model to predict invasive mechanical ventilation of COVID-19 patients within 24 h of an initial phase, employing data from the first 2 h after admission. A detailed explanation of the potential role of biomarkers in COVID-19 patients can be found in Malik et al 19 In the study of Ramo´n et al, 20 five ML models were deployed to predict the mortality rate of COVID-19 patients, including k-nearest neighbors(KNN), Gaussian nave Bayes (GNB), decision tree (DT), and support vector machine (SVM), where XGB achieved better performance in terms of accuracy (92%). Yadaw et al 21 proposed ML models, including XGBoost, Logistic Regression (LR), SVM, and Random Forest (RF), to predict the death risk of COVID-19 patients.…”