ArticleNeural computing & applications2021
SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.JMIR formative research · 2026Article
- Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department.BMC emergency medicine · 2025Article
- Can polycythaemia vera disease be predicted from haematologic parameters? A machine learning-based study.Journal of clinical pathology · 2025Article
- Using weak signals to predict spontaneous breathing trial success: a machine learning approach.Intensive care medicine experimental · 2025Article
- Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.BMC medical informatics and decision making · 2024Article
- A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.International journal of molecular sciences · 2024Article
- Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension.Respiratory research · 2024Article
- Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality.BMC infectious diseases · 2023Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Exploring obesity, physical activity, and digital game addiction levels among adolescents: A study on machine learning-based prediction of digital game addiction.Frontiers in psychology · 2023Article
- Quantitative analysis of fertilizer using laser-induced breakdown spectroscopy combined with random forest algorithm.Frontiers in chemistry · 2023Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
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Authors and funding
2 authors.
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Abstract
An increase in the number of patients and death rates make Covid-19 a serious pandemic situation. This problem has effects on health security, economical security, social life, and many others. The long and unreliable diagnosis process of the Covid-19 makes the disease spread even faster. Therefore, fast and efficient diagnosis is significant for dealing with this pandemic. Computer-aided medical diagnosis systems are very common applications and due to the importance of the problem, providing accurate predictions is required. In this study, blood samples of patients from Einstein Hospital in Brazil has collected and used for prediction on the severity level of Covid-19 with machine learning algorithms. The study was constructed in two stages; in stage-one, no preprocessing method has applied while in stage-two preprocessing has emphasized for achieving better prediction results. At the end of the study, 0.98 accuracy was obtained with the tuned Random Forest algorithm and several preprocessing methods.
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