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Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants
Tirualem Zeleke Yehuala1, Bezawit Melak Fente2, Sisay Maru Wubante1
1Department Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Insights
Predicting acute respiratory infections (ARI) in children under five using machine learning can save lives. Vaccination, breastfeeding, and facility birth are key protective factors against ARI in Sub-Saharan Africa.
Area of Science:
- Pediatrics
- Public Health
- Machine Learning
Background:
- Acute respiratory infections (ARI) are a leading cause of mortality in children under five globally.
- Early prediction and identification of ARI determinants are crucial for effective intervention strategies.
- Machine learning offers advanced tools for analyzing complex health datasets to predict and prevent childhood diseases.
Purpose of the Study:
- To predict acute respiratory infections (ARI) in children under five using machine learning models.
- To identify the key determinants and risk factors associated with ARI in Sub-Saharan Africa.
- To leverage advanced AI for reducing child mortality due to respiratory illnesses.
Main Methods:
- Utilized Demographic and Health Survey (DHS) data from 36 Sub-Saharan African countries (2005-2022).
- Employed five machine learning algorithms: Random Forest, Decision Tree, XGBoost, Logistic Regression, and Naive Bayes.
- Evaluated model performance using accuracy, precision, recall, and AUC curve metrics.
Main Results:
- Random Forest achieved the highest performance with 96.40% accuracy, 87.9% precision, and 94% ROC.
- Key protective factors against ARI include breastfeeding, vaccination, media exposure, absence of diarrhea, and facility birth.
- Naive Bayes showed the lowest performance among the tested models.
Conclusions:
- Machine learning, particularly Random Forest, demonstrates high predictive power for ARI in children.
- Vaccination status is a significant factor in preventing ARI, highlighting the importance of immunization programs.
- Findings support policy development to reduce infant mortality by addressing identified ARI risk factors.
Background:
The primary cause of death for children under the age of five is acute respiratory infections (ARI). Early predicting acute respiratory tract infections (ARI) and identifying their predictors using supervised machine learning algorithms is the most effective way to save the lives of millions of children. Hence, this study aimed to predict acute respiratory tract infections (ARI) and identify their determinants using the current state-of-the-art machine learning models.
Methods:
We used the most recent demographic and health survey (DHS) dataset from 36 Sub-Saharan African countries collected between 2005 and 2022. Python software was used for data processing and machine learning model building. We employed five machine learning algorithms, such as Random Forest, Decision Tree (DT), XGBoost, Logistic Regression (LR), and Naive Bayes, to analyze risk factors associated with ARI and predict ARI in children. We evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.
Result:
In this study, 75,827 children under five were used in the final analysis. Among the proposed machine learning models, random forest performed best overall in the proposed classifier, with an accuracy of 96.40%, precision of 87.9%, F-measure of 82.8%, ROC curve of 94%, and recall of 78%. Naïve Bayes accuracy has also achieved the least classification with accuracy (87.53%), precision (67%), F-score (48%), ROC curve (82%), and recall (53%). The most significant determinants of preventing acute respiratory tract infection among under five children were having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility. These were associated positively with the outcome variable.
Conclusion:
According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.
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