ArticleInternational journal of environmental research and public health2021
Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis.Frontiers in public health · 2024Pooled it
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Mortality Prediction from Patient's First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Endothelin-1 in combination with CRB-65 enhance risk stratification in COVID-19 patients.Infection · 2025Article
- Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.BMC geriatrics · 2024Article
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification.Machine learning and knowledge extraction · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Selected Predictors of COVID-19 Mortality in the Hospitalised Patient Population in a Single-Centre Study in Poland.Healthcare (Basel, Switzerland) · 2023Article
- A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.Sensors (Basel, Switzerland) · 2022Article
- Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.BMJ open · 2022Article
- Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.Sensors (Basel, Switzerland) · 2022Article
- Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.Healthcare (Basel, Switzerland) · 2022Article
- Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.Journal of healthcare engineering · 2022Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2021Review
- Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.