ArticleSN computer science2020
Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.
Article in SN computer science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Predicting software reuse using machine learning techniques-A case study on open-source Java software systems.PloS one · 2025Article
- Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.Infection prevention in practice · 2024Review
- An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.Multimedia tools and applications · 2023Article
- Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19.Interactive journal of medical research · 2023Article
- Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.SN computer science · 2023Article
- Survival study on deep learning techniques for IoT enabled smart healthcare system.Health and technology · 2023Article
- Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty.Frontiers in public health · 2023Observational
- Analysis of COVID-19 Death Cases Using Machine Learning.SN computer science · 2023Article
- Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.Frontiers in public health · 2023Article
- Enhancement of license plate recognition performance using Xception with Mish activation function.Multimedia tools and applications · 2023Article
- Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa.Environmental science and pollution research international · 2023Article
- COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans.Cognitive computation · 2022Article
- Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.BMC medical imaging · 2022Article
- Determining the efficiency of data analysis systems in predicting COVID-19 infected cases.Journal of family medicine and primary care · 2022Article
- Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach.GeoHealth · 2022Article
- Prognosis patients with COVID-19 using deep learning.BMC medical informatics and decision making · 2022Article
10 more citing papers are in PubMed but not listed here.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.
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