ArticlePloS one2024
Predicting malaria outbreak in The Gambia using machine learning techniques.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Explainable Machine Learning for Predicting Global Malaria Incidence From 2000 to 2022 With Climatic and Extreme Weather Conditions Across 78 Endemic Countries: A Retrospective Ecological Study.Health science reports · 2026Article
- Early malaria risk screening in Nigerian minors using AutoML and cluster-based analysis of non-clinical survey data.PLOS digital health · 2026Article
- Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.PLOS digital health · 2026Article
- Artificial intelligence for climate-health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action.Globalization and health · 2026Article
- Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.Infectious disease reports · 2026Article
- Multi-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS data.PLOS digital health · 2026Article
- Optimizing wild bird fecal surveillance sites for HPAI through risk estimation using machine learning algorithms.One health (Amsterdam, Netherlands) · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Artificial intelligence for precision malaria control: transforming surveillance, prediction, and intervention strategies.Frontiers in digital health · 2026Review
- Integrating machine learning and spatial clustering for malaria case prediction in Brazil's Legal Amazon.BMC infectious diseases · 2025Article
- AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications.Biology · 2025Review
- Projection of future malaria prevalence in the upper river region of The Gambia.Malaria journal · 2025Article
- Strengthening health information systems and inherent statistical outputs for improved malaria control and interventions in western Kenya.Frontiers in epidemiology · 2025Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic growth of nations in the tropical and subtropical parts of the world. This study aims to address this challenge by developing a predictive model for malaria outbreaks in each district of The Gambia, leveraging historical meteorological data. To achieve this objective, we employ and compare the performance of eight machine learning algorithms, including C5.0 decision trees, artificial neural networks, k-nearest neighbors, support vector machines with linear and radial kernels, logistic regression, extreme gradient boosting, and random forests. The models are evaluated using 10-fold cross-validation during the training phase, repeated five times to ensure robust validation. Our findings reveal that extreme gradient boosting and decision trees exhibit the highest prediction accuracy on the testing set, achieving 93.3% accuracy, followed closely by random forests with 91.5% accuracy. In contrast, the support vector machine with a linear kernel performs less favorably, showing a prediction accuracy of 84.8% and underperforming in specificity analysis. Notably, the integration of both climatic and non-climatic features proves to be a crucial factor in accurately predicting malaria outbreaks in The Gambia.
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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.