Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach

Risk factors associated with underweight in children aged one to two years: a longitudinal study

BACKGROUND

Underweight is a prevalent health issue in children. This study aimed to identify factors associated with underweight in children aged 1-2 years in Hamadan city. Unlike the studies conducted in this field, which are cross-sectional and do not provide information on the effect of age changes on underweight, our longitudinal approach provides insights into weight changes over time. On the other hand, this study focuses on the high-risk age group of 1 to 2 years, which has only been addressed in a few studies.

METHODS

In this longitudinal study, 414 mothers with 1 to 2 year-old children referred to the health centers of Hamadan city, whose information is in the SIB system, a comprehensive electronic system, were examined to identify factors related to underweight. The response variable was weight-for-age criteria classified into three categories: underweight, normal weight, and overweight. A two-level longitudinal ordinal model was used to determine the factors associated with underweight.

RESULTS

Of the children studied, 201 (48.6%) were girls and 213 (51.4%) were boys. Significant risk factors for underweight included low maternal education (AOR = 3.56, 95% CI: 1.10-11.47), maternal unemployment (AOR = 3.38, 95% CI: 1.05-10.91), maternal height (AOR = 0.85, 95% CI: 0.79-0.92), lack of health insurance (AOR = 2.85, 95% CI: 1.04-7.84), gestational age less than 24 years (AOR = 3.17, 95% CI: 16.28-0.97), child age 12-15 months (AOR = 2.27, 95% CI: 1.37-3.74), and child's birth weight (AOR = 0.63, 95% CI: 0.70-0.58).

CONCLUSION

Based on the results of the present study, it seems that the possibility of being underweight among children is more related to the characteristics of mothers; therefore, taking care of mothers can control some of the weight loss of children.

Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

BACKGROUND

Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.

METHODS

Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.

RESULTS

The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities.

CONCLUSION

This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.

Machine learning prediction of nutritional status among pregnant women in Bangladesh: Evidence from Bangladesh demographic and health survey 2017-18

AIM

Malnutrition in pregnant women significantly affects both mother and child health. This research aims to identify the best machine learning (ML) techniques for predicting the nutritional status of pregnant women in Bangladesh and detect the most essential features based on the best-performed algorithm.

METHODS

This study used retrospective cross-sectional data from the Bangladeshi Demographic and Health Survey 2017-18. Different feature transformations and machine learning classifiers were applied to find the best transformation and classification model.

RESULTS

This investigation found that robust scaling outperformed all feature transformation methods. The result shows that the Random Forest algorithm with robust scaling outperforms all other machine learning algorithms with 74.75% accuracy, 57.91% kappa statistics, 73.36% precision, 73.08% recall, and 73.09% f1 score. In addition, the Random Forest algorithm had the highest precision (76.76%) and f1 score (71.71%) for predicting the underweight class, as well as an expected precision of 82.01% and f1 score of 83.78% for the overweight/obese class when compared to other algorithms with a robust scaling method. The respondent's age, wealth index, region, husband's education level, husband's age, and occupation were crucial features for predicting the nutritional status of pregnant women in Bangladesh.

CONCLUSION

The proposed classifier could help predict the expected outcome and reduce the burden of malnutrition among pregnant women in Bangladesh.

Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

BACKGROUND

This paper presents an in-depth examination of malnutrition in women in Bangladesh. Malnutrition in women is a major public health issue related to different diseases and has negative repercussions for children, such as premature birth, decreased infection resistance, and an increased risk of death. Moreover, malnutrition is a severe problem in Bangladesh. Data from the Bangladesh Demographic Health Survey (BDHS) conducted in 2017-18 was used to identify risk factors for malnourished women and to create a machine learning-based strategy to detect their nutritional status.

METHODS

A total of 17022 women participants are taken to conduct the research. All the participants are from different regions and different ages. A chi-square test with a five percent significance level is used to identify possible risk variables for malnutrition in women and six machine learning-based classifiers (Naïve Bayes, two types of Decision Tree, Logistic Regression, Random Forest, and Gradient Boosting Machine) were used to predict the malnutrition of women. The models are being evaluated using different parameters like accuracy, sensitivity, specificity, positive predictive value, negative predictive value, [Formula: see text] score, and area under the curve (AUC).

RESULTS

Descriptive data showed that 45% of the population studied were malnourished women, and the chi-square test illustrated that all fourteen variables are significantly associated with malnutrition in women and among them, age and wealth index had the most influence on their nutritional status, while water source had the least impact. Random Forest had an accuracy of 60% and 60.2% for training and test data sets, respectively. CART and Gradient Boosting Machine also had close accuracy like Random Forest but based on other performance metrics such as kappa and [Formula: see text] scores Random Forest got the highest rank among others. Also, it had the highest accuracy and [Formula: see text] scores in k-fold validation along with the highest AUC (0.604).

CONCLUSION

The Random Forest (RF) approach is a reasonably superior machine learning-based algorithm for forecasting women's nutritional status in Bangladesh in comparison to other ML algorithms investigated in this work. The suggested approach will aid in forecasting which women are at high susceptibility to malnutrition, hence decreasing the strain on the healthcare system.

Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea

Preventing stunting is particularly important for healthy development across the life course. In Papua New Guinea (PNG), the prevalence of stunting in children under five years old has consistently not improved. Therefore, the primary objective of this study was to employ multiple machine learning algorithms to identify the most effective model and key predictors for stunting prediction in children in PNG. The study used data from the 2016-2018 Papua New Guinea Demographic Health Survey, including from 3380 children with complete height-for-age data. The least absolute shrinkage and selection operator (LASSO) and random-forest-recursive feature elimination were used for feature selection. Logistic regression, a conditional decision tree, a support vector machine with a radial basis function kernel, and an extreme gradient boosting machine (XGBoost) were employed to construct the prediction model. The performance of the final model was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of the study showed that LASSO-XGBoost has the best performance for predicting stunting in PNG (AUC: 0.765; 95% CI: 0.714-0.819) with accuracy, precision, recall, and F1 scores of 0.728, 0.715, 0.628, and 0.669, respectively. Combined with the SHAP value method, the optimal prediction model identified living in the Highlands Region, the age of the child, being in the richest family, and having a larger or smaller birth size as the top five important characteristics for predicting stunting. Based on the model, the findings support the necessity of preventing stunting early in life. Emphasizing the nutritional status of vulnerable maternal and child populations in PNG is recommended to promote maternal and child health and overall well-being.

Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren

Introduction

Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors.

Methods

This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children. Our first analysis used the entire dataset and then we reran this analysis on age, residence, and sex population subsets.

Results

Both logistic regression and ML methods identified older childhood age as a significant risk factor, while females and vaccinated individuals showed reduced odds of stunting. Our machine learning analyses provided additional insights into the data, as feature selection identified that age, school latrine cleanliness, large family size, and nail trimming habits were significant risk factors for stunting, underweight, and thinness. Association rule learning revealed an association between co-occurring hygiene and socio-economical variables with malnutrition that was otherwise missed using traditional statistical methods.

Discussion

Our analysis supports the benefit of integrating feature selection methods, association rules learning techniques, and logistic regression to identify comprehensive risk factors associated with malnutrition in young children.

A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries

BACKGROUND

The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa.

METHODS

This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.

RESULTS

We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).

CONCLUSIONS

The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.

Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach

BACKGROUND

Globally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia.

METHODOLOGY

Secondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software.

RESULT

Random forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women's education level, family size, husband's desire for children, husband's education level, and women's fertility preference as predictors most frequently associated with contraceptive discontinuation.

CONCLUSION

Results have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.

Factors Related to Underweight Prevalence among 33,776 Children Below 60 Months Old Living in Northern Geopolitical Zones, Nigeria (2008–2018)

The prevalence of underweight among children below 60 months old in Nigeria remains a significant public health challenge, especially in northern geopolitical zones (NGZ), ranging from 15% to 35%. This study investigates time-based trends in underweight prevalence and its related characteristics among NGZ children below 60 months old. Extracted NGZ representative dataset of 33,776 live births from the Nigeria Demographic and Health Survey between 2008 and 2018 was used to assess the characteristics related to underweight prevalence in children aged 0–23, 24–59, and 0–59 months using multilevel logistics regression. Findings showed that 11,313 NGZ children below 60 months old were underweight, and 24–59-month-old children recorded the highest prevalence (34.8%; 95% confidence interval: 33.5–36.2). Four factors were consistently significantly related to underweight prevalence in children across the three age groups: poor or average-income households, maternal height, children who had diarrhoea episodes, and children living in the northeast or northwest. Intervention initiatives that include poverty alleviation through cash transfer, timely health checks of offspring of short mothers, and adequate clean water and sanitation infrastructure to reduce the incidence of diarrhoea can substantially reduce underweight prevalence among children in NGZ in Nigeria.

The Partial Least Squares Spline Model for Public Health Surveillance Data

Factor discovery of public health surveillance data is a crucial problem and extremely challenging from a scientific viewpoint with enormous applications in research studies. In this study, the main focus is to introduce the improved survival regression technique in the presence of multicollinearity, and hence, the partial least squares spline modeling approach is proposed. The proposed method is compared with the benchmark partial least squares Cox regression model in terms of accuracy based on the Akaike information criterion. Further, the optimal model is practiced on a real data set of infant mortality obtained from the Pakistan Demographic and Health Survey. This model is implemented to assess the significant risk factors of infant mortality. The recommended features contain key information about infant survival and could be useful in public health surveillance-related research.

Risk Factors of Stunting and Wasting among Children Aged 6–59 Months in Household Food Insecurity of Jima Geneti District, Western Oromia, Ethiopia: An Observational Study

Undernutrition is the most difficult and widespread public health concern in low-income nations including Ethiopia. Therefore, this study aimed to investigate the associated risk factors of stunting and wasting among children aged 6–59 months in Jima Geneti district, Western Oromia, Ethiopia. A community-based cross-sectional study was conducted on 500 children from December 1 to 28, 2020. A multiple-stage sampling method was performed to select children from each kebele. Anthropometric measurements were taken, and the nutritional status was generated using WHO Anthro v. 3.2.1. Data analysis was performed using the SPSS version 20.0. Bivariate and multivariate logistic regression analyses were carried out to identify the associated risk factors of stunting and wasting among children in the study area. Statistical significance was set at p < 0.05. The study results showed that the prevalence of stunting and wasting among children was 27% and 11.8%, respectively. The findings of this study also revealed that the prevalence of household food insecurity and poor dietary diets was 19.6% and 52.2%, respectively. Low wealth status (AOR = 2.5; 95% CI: 1.1, 5.55) and poor dietary diets (AOR = 4.7; 95% CI: 2.5, 8.83) were associated risk factors for stunting. However, child meal frequency (AOR = 3.9; 95% CI: 1.23, 12.6), and children who did feed leftover food (AOR = 2.75; 95% CI: 1.02, 7.44) were associated risk factors for wasting. Poor dietary diets (AOR = 2.65; 95% CI: 1.06, 6.66) were also associated risk factors for wasting. The findings of this study concluded that the prevalence of stunting and wasting was high in the study area. Therefore, addressing family-level risk factors which are major drivers of children's nutritional status is crucial to ensure the nutritional status of children.

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

Background

Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.

Method

The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models.

Results

Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban–rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones.

Conclusion

Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01652-1.