ArticleBMC infectious diseases2023
Spatial distribution and machine learning prediction of sexually transmitted infections and associated factors among sexually active men and women in Ethiopia, evidence from EDHS 2016.
Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 18 citations in OpenAlex.
- Application of machine learning algorithms for prediction of abortion and its determinants among women of reproductive-age in East African countries.Scientific reports · 2026Article
- Predictors of self-reported sexually transmitted infections (STIs) among men in 54 low and middle income countries (LMICs): a comparison of deep learning and classical machine learning algorithms.BMC infectious diseases · 2026Article
- Design a model to predict incomplete immunization among Ethiopian children using ensemble machine learning algorithms.Scientific reports · 2025Article
- How place shapes genital herpes simplex distribution in South Korea: a Bayesian spatial analysis using National Health Insurance Service data.BMC public health · 2025Article
- Prevalence and Spatial distribution characteristics of human echinococcosis in Ningxia Hui Autonomous Region, 2011-2023.BMC public health · 2025Article
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- Enhancing HIV/STI decision-making: challenges and opportunities in leveraging predictive models for individuals, healthcare providers, and policymakers.Journal of translational medicine · 2024Review
- Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.Scientific reports · 2024Article
- Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
introductionSexually transmitted infections (STIs) are the major public health problem globally, affecting millions of people every day. The burden is high in the Sub-Saharan region, including Ethiopia. Besides, there is little evidence on the distribution of STIs across Ethiopian regions. Hence, having a better understanding of the infections is of great importance to lessen their burden on society. Therefore, this article aimed to assess predictors of STIs using machine learning techniques and their geographic distribution across Ethiopian regions. Assessing the predictors of STIs and their spatial distribution could help policymakers to understand the problems better and design interventions accordingly.
methodsA community-based cross-sectional study was conducted from January 18, 2016, to June 27, 2016, using the 2016 Ethiopian Demography and Health Survey (EDHS) dataset. We applied spatial autocorrelation analysis using Global Moran's I statistics to detect latent STI clusters. Spatial scan statics was done to identify local significant clusters based on the Bernoulli model using the SaTScan™ for spatial distribution and Supervised machine learning models such as C5.0 Decision tree, Random Forest, Support Vector Machine, Naïve Bayes, and Logistic regression were applied to the 2016 EDHS dataset for STI prediction and their performances were analyzed. Association rules were done using an unsupervised machine learning algorithm.
resultsThe spatial distribution of STI in Ethiopia was clustered across the country with a global Moran's index = 0.06 and p value = 0.04. The Random Forest algorithm was best for STI prediction with 69.48% balanced accuracy and 68.50% area under the curve. The random forest model showed that region, wealth, age category, educational level, age at first sex, working status, marital status, media access, alcohol drinking, chat chewing, and sex of the respondent were the top 11 predictors of STI in Ethiopia.
conclusionApplying random forest machine learning algorithm for STI prediction in Ethiopia is the proposed model to identify the predictors of STIs.
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