ArticlePreventive medicine reports2021
A predictive model, and predictors of under-five child malaria prevalence in Ghana: How do LASSO, Ridge and Elastic net regression approaches compare?
Article in Preventive medicine reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 33 citations in OpenAlex.
- The Clinical Risk Factors of Adenovirus Pneumonia in Children Based on the Logistic Regression Model: Correlation with Lactate Dehydrogenase.International journal of clinical practice · 2022Trial
- Multi-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS data.PLOS digital health · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Artificial intelligence-assisted UV-Vis spectrophotometric method for simultaneous quantification of naringin and naringenin in Thymus canoviridis Jalas.BMC chemistry · 2026Article
- Assessment of Lasso and Ridge models for soil swelling potential prediction.Scientific reports · 2026Article
- Integrating vulnerability and hazard in malaria risk mapping: the elimination context of Senegal.BMC infectious diseases · 2025Article
- Malaria prevalence dynamics and risk covariates among children under 5 in Ghana: insights from a Bayesian multilevel approach.BMJ open · 2025Article
- Associations of temperature and precipitation with malaria in children under 5: A multi-country study in Sub-Saharan Africa.PloS one · 2025Article
- Factors associated with malaria infection in under-5 children in Papua Province, Indonesia: an observational study.Osong public health and research perspectives · 2024Article
- Antibodies to specific domains ofHealth science reports · 2024Article
- Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma.World journal of urology · 2024Article
- Screening of genes characteristic of pancreatic cancer by LASSO regression combined with support vector machine and recursive feature elimination, and immune correlation analysis.The Journal of international medical research · 2024Article
- Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area.Environmental geochemistry and health · 2024Article
- Malaria amongst children under five in sub-Saharan Africa: a scoping review of prevalence, risk factors and preventive interventions.European journal of medical research · 2023Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Malaria is among the leading causes of mortality and morbidity among children in Ghana. Therefore, identifying the predictors of malaria prevalence in children under-five is among the priorities of the global health agenda. In Ghana, the paradigm shifts from using traditional statistics to machine learning techniques to identifying predictors of malaria prevalence are scarce. Thus, the present study used machine learning techniques to identify variables to build the best fitting predictive model of malaria prevalence in Ghana. We analysed the data on 2867 under-five children with malaria RDT results from the 2019 Ghana Malaria Indicator Survey. LASSO, Ridge, and Elastic Net regression methods were used to select variables to build predictive models. The R freeware version 4.0.2 was used. One out of four children tested positive for malaria (25.04%). The logit models based on selected features by LASSO, Ridge, and Elastic Net contained eleven, fifteen, and thirteen features, respectively. The LASSO regression model is preferred because it contains the smallest number of predictors and the smallest prediction error. The significant predictors of malaria among children were being older than 24 months, residing in the poorest household, being severely anaemic, residing in households without electricity, and residing in a rural area. The predictors identified in our study deserve policy attention and interventions to strengthen malaria control efforts in Ghana. The machine learning techniques employed in our study, especially the LASSO regression technique could be beneficial for identifying predictors of malaria prevalence in this group of children.
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