ArticleIEEE access : practical innovations, open solutions2021
A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search.Scientific reports · 2025Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates.Sensors (Basel, Switzerland) · 2023Article
- A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.Wireless personal communications · 2023Review
- Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.International journal of environmental research and public health · 2022Article
- Diagnosis Methods for COVID-19: A Systematic Review.Micromachines · 2022Review
- Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.Journal of personalized medicine · 2022Article
- Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study.International journal of environmental research and public health · 2022Article
- Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage.Bioengineering (Basel, Switzerland) · 2022Article
- An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP.Diagnostics (Basel, Switzerland) · 2022Article
- Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.Computers in biology and medicine · 2022Article
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
- Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.Computational and mathematical methods in medicine · 2021Article
- Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images.Journal of healthcare engineering · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.
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