Prevalence of malnutrition and associated factors in Chinese children and adolescents aged 3-14 years using machine
Fangjieyi Zheng1, Kening Chen2, Xiaoqian Zhang3,4
1Centre for Evidence-Based Medicine, Capital Institute of Paediatrics, Beijing, People's Republic of China.
Insights
Child malnutrition affects 10.93% of children aged 3-14. Machine learning identified eight key factors, including diet and lifestyle, for predicting malnutrition risk in children.
Area of Science:
- Pediatric Nutrition
- Public Health
- Computational Biology
Background:
- Child malnutrition is a significant global health concern with lasting developmental impacts.
- Understanding its causes is crucial for effective prevention and intervention strategies.
- This study addresses the need for identifying key factors contributing to malnutrition in children.
Purpose of the Study:
- To determine the prevalence of malnutrition in children and adolescents aged 3-14 years.
- To identify significant factors associated with child malnutrition using advanced machine learning algorithms.
- To develop a predictive model for child malnutrition.
Main Methods:
- A cross-sectional study involving 18,503 children and adolescents from Beijing and Tangshan.
- Malnutrition assessment based on World Health Organization (WHO) standards.
- Logistic regression and ensemble learning algorithms, including Random Forest, were employed for factor identification and model building.
Main Results:
- The prevalence of malnutrition was found to be 10.93% in the surveyed population.
- The Random Forest model demonstrated high predictive accuracy (AUROC = 0.929).
- Eight key predictive factors were identified: age, fast food intake, late-night snacking, family history of diabetes, breastfeeding duration, sedentary time, and parental body mass index.
Conclusions:
- Eight significant factors contributing to malnutrition in children aged 3-14 have been identified.
- The Random Forest model provides an optimal approach for predicting child malnutrition based on these factors.
- Further validation in independent populations is recommended to confirm these findings.
Background:
Child malnutrition represents a critical global public health issue and it is characterised by high prevalence and severe long-term consequences for growth and development. A better understanding of its contributory factors is essential to inform the design of targeted prevention strategies and evidence-based interventions. We aimed to estimate the prevalence of malnutrition in children and adolescents aged 3-14 years, and further to identify promising factors associated with child malnutrition using machine learning algorithms.
Methods:
Thirty kindergartens and 26 schools were randomly selected from Beijing and Tangshan. Child malnutrition was defined according to WHO standards. Factors for child malnutrition were selected by Logistic regression and three ensemble learning algorithms. An open-access web platform was developed to facilitate calculating probabilities of child malnutrition.
Results:
Total 18 503 children and adolescents were surveyed, and 10.93% (n = 2022) of them were found to be malnourished. Random forest emerged as the best model, as it carried the highest area under the receiver operating characteristic curve (AUROC) at 0.929. Under the implementation of random forest, top eight factors that formed the optimal set for child malnutrition prediction were identified, including age, frequency of fast food intake, frequency of late-night snacking, family history of diabetes, duration of breastfeeding, sedentary time, and parental body mass index. Further Logistic regression analyses confirmed the predictive significance of these individual factors.
Conclusions:
We have identified eight contributory factors for malnutrition in 3-14-year-old children and adolescents in Beijing and Tangshan, with their prediction performance optimal under random forest. More studies among independent populations are warranted to validate our findings.
