Exploring Risk Factors and Predictive Modeling of Child Malnutrition in Pakistan Using Machine Learning
Muhammad Usman Saleem1, Muhammad Usman Aslam2, Abdul Ghani Khatir1
1School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Insights
Machine learning models effectively predict child malnutrition in Pakistan, identifying key risk factors like low maternal education and poverty. These findings support targeted interventions for vulnerable populations.
Area of Science:
- Pediatric Nutrition
- Computational Epidemiology
- Public Health Informatics
Background:
- Child malnutrition, encompassing stunting, wasting, and underweight, remains a critical public health issue in Pakistan.
- Socio-demographic and health-related factors significantly influence malnutrition prevalence in children under five.
Purpose of the Study:
- To identify risk factors associated with child malnutrition in Pakistan.
- To develop and evaluate machine learning models for predicting child malnutrition (stunting, wasting, underweight).
- To inform targeted public health interventions.
Main Methods:
- Cross-sectional analysis of Pakistan Demographic and Health Survey (PDHS) 2017-2018 data.
- Logistic regression for risk factor identification.
- Application and performance evaluation of Random Forest, SVM, Naïve Bayes, and AdaBoost models using cross-validation and train-test splits.
Main Results:
- Consanguineous marriages, lower wealth, low maternal education, and regional disparities (Sindh, Baluchistan) were identified as significant risk factors.
- Random Forest model achieved the highest accuracy and specificity for predicting malnutrition indicators.
- Support Vector Machine (SVM) demonstrated superior sensitivity for wasted and underweight children.
Conclusions:
- Child malnutrition in Pakistan is linked to a complex interplay of socio-demographic factors.
- Machine learning offers a powerful approach for predicting child malnutrition.
- Interventions should prioritize maternal education, sanitation, and poverty reduction in high-risk areas.
Objective:
This study aims to identify risk factors and develop predictive models of child malnutrition (stunting, wasting, and underweight) in Pakistani children under five using machine learning approaches.
Study Design:
This cross-sectional design utilized data from the Pakistan Demographic and Health Survey 2017-2018 (PDHS).
Methods:
Logistic regression was employed to identify significant socio-demographic and health-related risk factors. Four machine learning models-Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and AdaBoost-were applied to predict malnutrition indicators, with performance evaluated based on accuracy, sensitivity, specificity, and F-measure using a two-stage validation strategy (10-fold cross-validation and 80:20 train-test split).
Results:
Key risk factors identified included consanguineous marriages, lower wealth status, low maternal education, and geographic disparities in Sindh and Baluchistan. Among the machine learning models, Random Forest demonstrated the highest overall accuracy and specificity across all indicators, while SVM showed higher sensitivity for wasted and underweight children.
Conclusion:
The study highlights the complex interplay of socio-demographic factors in child malnutrition and the potential of machine learning models to effectively predict these conditions, underscoring the need for targeted interventions focusing on maternal education, access to clean water and sanitation, and poverty alleviation, particularly in high-risk regions.
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