Related Experiment Video
Updated: Mar 27, 2026

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples
Published on: April 25, 2025
Modeling diarrhea in children under five in Somaliland: A machine learning analysis using SLDHS 2020 data
Yahye Hassan Muse1, Mukhtar Abdi Hassan1, Abdisalam Hassan Muse1
1Faculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama, Somalia.
Insights
Diarrhea affects 7.2% of children under five in Somaliland, with nomadic and low-income households most impacted. Improving water and sanitation access is crucial for public health strategies.
Area of Science:
- Public Health
- Epidemiology
- Pediatrics
Background:
- Diarrhea is a major cause of illness and death in children under five, especially in low- and middle-income countries.
- This study examines diarrhea prevalence and its determinants in Somaliland using the 2020 Somaliland Health and Demographic Survey (SLDHS).
Purpose of the Study:
- To investigate the prevalence and identify key determinants of childhood diarrhea in Somaliland.
- To inform targeted public health interventions for diarrhea prevention and control.
Main Methods:
- Cross-sectional study analyzing data from 1,112 women and their children under five.
- Descriptive statistics, binary logistic regression, and supervised machine learning models (Logistic Regression, Probit, Random Forest, Decision Tree, SVM) were used.
- Variables included socioeconomic, demographic, and environmental factors.
Main Results:
- Overall diarrhea prevalence was 7.2% with significant regional variations.
- Nomadic households showed higher incidence (8.62%) compared to rural and urban households.
- Region, household wealth, and sanitation access were significant predictors; maternal education was not.
Conclusions:
- Region-specific public health strategies are vital, focusing on water and sanitation access for nomadic and low-income populations.
- Machine learning models showed high overall accuracy but require refinement for predicting positive diarrhea cases.
- Enhancing infrastructure and sanitation in underserved communities is a priority to reduce childhood diarrhea.
Background:
Diarrhea remains a leading cause of morbidity and mortality among children under five years of age, particularly in low- and middle-income countries. This study investigated the prevalence and determinants of diarrhea in Somaliland using nationally representative data from the 2020 Somaliland Health and Demographic Survey (SLDHS) 2020.
Methods:
We employed a cross-sectional study design and analyzed data from 1,112 women (aged 15-49) and their children under the age of five from six geographic regions in Somaliland. Variables were selected based on data availability in SLDHS 2020, including socioeconomic, demographic, and environmental factors. We employed descriptive statistics and binary logistic regression to identify significant associations of the variables with childhood diarrhea. Additionally, supervised machine-learning models (Logistic Regression, Probit Regression, Random Forest, Decision Tree, and SVM) were used to identify key determinants of diarrhea.
Results:
The overall prevalence of diarrhea was 7.2%, with significant regional variation (Togdheer: 12.5%; Awdal: 4.24%). Nomadic households had a significantly higher incidence (8.62%) than rural (2.41%) and urban (5.16%) households. Logistic regression analysis highlighted region, household wealth index, and sanitation access as significant predictors. Interestingly, maternal educational level was not significantly associated with the prevalence of diarrhea. The Decision Tree model achieved the highest accuracy (92.3%) and sensitivity (33.3%), while Logistic Regression had specificity >97%.
Conclusion:
This study underscores the importance of region-specific public health strategies focused on improving access to water and sanitation, especially in nomadic and low-income populations. Despite the high overall accuracy, the machine-learning models indicated that the predictive accuracy for positive diarrhea cases could be further refined. Efforts to alleviate diarrhea among young children in Somaliland should prioritize the enhancement of infrastructure and sanitation resources in underserved communities.
Related Concept Videos
Steps in Outbreak Investigation
Statistical Methods for Analyzing Epidemiological Data
Investigation of Disease Outbreaks
Drugs Affecting GI Tract Motility: Antimicrobials as Antidiarrheal Agents
Principles of Disease Surveillance

