ArticleChaos, solitons, and fractals2020
Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Integrative transcriptomics and machine learning reveal key regulatory genes for meat quality traits in pigs.BMC genomics · 2026Article
- Integrating social cognitive theory with machine learning to predict MSM-women sexual behavior: a multicenter random forest model development study in China.Scientific reports · 2026Article
- Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms.Animals : an open access journal from MDPI · 2024Article
- A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning based method for analyzing vibration and noise in large cruise ships.PloS one · 2024Article
- S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality.PloS one · 2024Article
- Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.Biophysics reviews · 2023Review
- Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.The Science of the total environment · 2023Article
- Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning.Diagnostics (Basel, Switzerland) · 2023Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist.Pediatric neurology · 2023Review
- Data Mining Based Techniques for Covid-19 Predictions.Procedia computer science · 2023Article
- Analysis of COVID-19 Death Cases Using Machine Learning.SN computer science · 2023Article
- Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.SN computer science · 2023Article
- Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.Sensors (Basel, Switzerland) · 2022Article
- Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach.Scientific reports · 2022Article
- A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy.Journal of biomedical informatics · 2022Article
- Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months. However, the rate of increase in cases was not equal for every country. The measures implemented by the countries changed the daily spreading speed of the disease. This was determined by changes in the number of daily cases. In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results. The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) and estimating sub-data (extrapolation data) for the random forest model. At the end of the study, it has been found that R
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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.