A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Objective
The recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach.
Research Design and Methods
Clinical data of 1085 cases of COVID-19 from 13 th January to 28 th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were selected for the final analysis. Random Forest classification algorithm was implemented on the dataset to identify the important predictors and their effects on mortality.
Results
The Area under the ROC curve obtained during model validation on the test dataset was 0.97. Age was the most important variable in predicting mortality followed by the time gap between symptom onset and hospitalization.
Conclusions
Patients aged beyond 62 years are at higher risk of fatality whereas hospitalization within 2 days of the onset of symptoms could reduce mortality in COVID-19 patients.
Article activity feed
-
SciScore for 10.1101/2020.03.25.20043331: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization The dataset was randomly split into training and test dataset containing 70% and 30% of the total samples respectively. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Machine Learning Algorithm and Statistical Analysis: Random Forest classification algorithm (4) was implemented over a dataset with 37 deaths and 78 recoveries using the randomForest package in R. randomForestsuggested: (RandomForest Package in R, RRID:SCR_015718)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged …
SciScore for 10.1101/2020.03.25.20043331: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization The dataset was randomly split into training and test dataset containing 70% and 30% of the total samples respectively. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Machine Learning Algorithm and Statistical Analysis: Random Forest classification algorithm (4) was implemented over a dataset with 37 deaths and 78 recoveries using the randomForest package in R. randomForestsuggested: (RandomForest Package in R, RRID:SCR_015718)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
