ArticlePeerJ2020
A descriptive study of random forest algorithm for predicting COVID-19 patients outcome.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis.Clinical reviews in allergy & immunology · 2023Pooled it
- Explainable machine learning-based mortality risk stratification for older adults with COVID-19: pinpointing core immunological biomarkers and revealing dose-threshold effects.Frontiers in immunology · 2026Article
- Identification of Mitochondrial and Succinylation Modification-Related Gene Signature in Ischemic Stroke.Molecular neurobiology · 2025Article
- COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.BMC public health · 2025Article
- Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms.Viruses · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients.PeerJ. Computer science · 2023Article
- Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.Sensors (Basel, Switzerland) · 2022Article
- Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data.Computers in biology and medicine · 2022Article
- Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran.BMC medical informatics and decision making · 2022Article
- Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial.Journal of clinical medicine · 2022Article
- Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic.Scientific reports · 2021Article
- Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models.Computational urban science · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients.
methodsThe clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients' clinical prognoses.
resultsSeven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42-16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13-11.25]).
conclusionWe applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.
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