ArticleJournal of big data2022
Machine learning approaches in Covid-19 severity risk prediction in Morocco.
Article in Journal of big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- COVID-19 severity analysis for clinical decision support based on machine learning approach.Scientific reports · 2025Article
- Enhanced deep learning framework for real-time instrument detection and tracking in laparoscopic surgery using advanced augmentation and tracking techniques.Surgical endoscopy · 2025Article
- Gut microbiota alterations are linked to COVID-19 severity in North African and European populations.NPJ biofilms and microbiomes · 2025Article
- Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.Scientific reports · 2025Article
- Predicting Intensive Care Unit Admission in COVID-19-Infected Pregnant Women Using Machine Learning.Journal of clinical medicine · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.Journal of family medicine and primary care · 2024Article
- Excess of body weight is associated with accelerated T-cell senescence in hospitalized COVID-19 patients.Immunity & ageing : I & A · 2024Article
- Article
- Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches.BMC nutrition · 2024Article
- Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model.Biomimetics (Basel, Switzerland) · 2023Article
- Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission.PLoS pathogens · 2023Article
- Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates.Intelligent systems with applications · 2023Article
- Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME.Scientific reports · 2023Article
- Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.Scientific reports · 2023Article
- Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2022Article
- Time-Dependent Changes of Laboratory Parameters as Independent Predictors of All-Cause Mortality in COVID-19 Patients.Biology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.
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Registered trials
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