ReviewHeliyon2021
The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review.
Review in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed.
- Modelling African Swine Fever Transmission and Epidemiology: A Scoping Review of Mechanistic, Statistical, and Machine Learning Approaches.Tropical medicine and infectious disease · 2026Review
- Socioeconomic Interventions for WHO's End TB Strategy Targets: Insights from SIR Modelling in Kazakhstan.International journal of environmental research and public health · 2026Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.BMC cardiovascular disorders · 2025Article
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- Seasonal and Meteorological Drivers of Hand, Foot, and Mouth Disease Outbreaks Using Data-Driven Machine Learning Models.Tropical medicine and infectious disease · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Spatiotemporal risk of human brucellosis under intensification of livestock keeping based on machine learning techniques in Shaanxi, China.Epidemiology and infection · 2024Article
- Arabic dialect identification in social media: A hybrid model with transformer models and BiLSTM.Heliyon · 2024Article
- Climate Change and Infectious Diseases: Navigating the Intersection through Innovation and Interdisciplinary Approaches.International journal of environmental research and public health · 2024Article
- Article
- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
- Article
- A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention.International journal of environmental research and public health · 2023Article
- Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya.PLOS digital health · 2023Article
- Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.Frontiers in immunology · 2023Article
- Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.One health (Amsterdam, Netherlands) · 2022Review
- Observing flow of He II with unsupervised machine learning.Scientific reports · 2022Article
- Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce.Healthcare (Basel, Switzerland) · 2022Review
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms
Indexed as
Identifiers
What OpenQuestion holds
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.