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

Kebede, Shimels Derso; Sebastian, Yakub; Yeneneh, Abraham; Chanie, Ashenafi Fentahun; Melaku, Mequannent Sharew; Walle, Agmasie Damtew

doi:10.1186/s12911-023-02102-w

Cited by 14 publications

(11 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These implications pave the way for targeted interventions, personalized healthcare approaches, and improved health outcomes for individuals affected by anemia. This finding aligns with similar studies conducted in Rwanda 18 and Ethiopia 61 , which revealed that the RF model outperformed the other ML models with a slight difference on the value of evaluation metrics. This slight difference might be due to the size of the data set used for model building across the studies.…”

Section: Discussionsupporting

confidence: 91%

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

Zemariam,

Yimer,

Abebe

et al. 2024

Sci Rep

View full text Add to dashboard Cite

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls.

show abstract

Section: Discussionsupporting

confidence: 91%

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

Zemariam,

Yimer,

Abebe

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Generating rules for childhood vaccination was the third objective of the study. Previous studies have assessed the joint effect of independent predictors on the outcome of interest [ 44 , 70 , 78 ]. Consequently, seven association rules were generated to determine vaccination status among children aged 12–23 months in Ethiopia.…”

Section: Discussionmentioning

confidence: 99%

“…These rules are critically important for the prevention and control of health problems and crucial for health policymakers’ proactive decision-making purposes. Various studies have widely used if/then rules in healthcare research, such as predicting childhood care and child mortality [ 66 ], predicting parasite infection [ 67 ], the pattern of new cases and stroke [ 68 , 69 ], and maternal healthcare service utilization discontinuation to identify important features [ 70 ]. The relationship between X and Y attributes is expressed in the following way [ 69 ].…”

Section: Prediction and Association Rule Miningmentioning

confidence: 99%

Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

Demsash,

Chereka,

Walle

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Introduction Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12–23 months using the best machine learning algorithm. Methods A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12–23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 70% and 30% of the observations were used for training, and evaluating the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. All the included algorithms were evaluated using confusion matrix elements. The synthetic minority oversampling technique was used for imbalanced data management. Informational gain value was used to select important attributes to predict childhood vaccination. The If/ then logical association was used to generate rules based on relationships among attributes, and Weka version 3.8.6 software was used to perform all the prediction analyses. Results PART was the first best machine learning algorithm to predict childhood vaccination with 95.53% accuracy. J48, multilayer perceptron, and random forest models were the consecutively best machine learning algorithms to predict childhood vaccination with 89.24%, 87.20%, and 82.37% accuracy, respectively. ANC visits, institutional delivery, health facility visits, higher education, and being rich were the top five attributes to predict childhood vaccination. A total of seven rules were generated that could jointly determine the magnitude of childhood vaccination. Of these, if wealth status = 3 (Rich), adequate ANC visits = 1 (yes), and residency = 2 (Urban), then the probability of childhood vaccination would be 86.73%. Conclusions The PART, J48, multilayer perceptron, and random forest algorithms were important algorithms for predicting childhood vaccination. The findings would provide insight into childhood vaccination and serve as a framework for further studies. Strengthening mothers’ ANC visits, institutional delivery, improving maternal education, and creating income opportunities for mothers could be important interventions to enhance childhood vaccination.

show abstract

“…In this study supervised machine learning algorithms such as Random Forest, Ada Boost, Gaussian NB, MLP, Decision Tree, Logistic Regression (LR), random forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XG Boost), and support vector machines (SVM) (25)(26)(27)(28), was performed to predict determinants of home delivery after ANC visit among reproductive age women in East Africa. A tenfold cross-validation method was used for training the models on training data.…”

Section: Data Management and Analysismentioning

confidence: 99%

Machine learning algorithm to predict determinants of home delivery after ANC visit among reproductive age women in East Africa: Using SHAP

Walle,

Kebede,

Adem

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Home birth is described as a delivery that takes place at home without the presence of a skilled birth attendant. Home delivery after ANC visit is a major public health concern, and reducing the proportion of home births in East Africa is a key strategy for lowering the maternal death rate. However, no studies on this public health issue. Therefore, this study aimed to assess the machine learning approach to predict determinants of home delivery after ANC visits among reproductive-age women in East Africa. Methods: A community-based, cross-sectional study was conducted using a recent Demographic and Health Survey (DHS) from 2011 to 2021 data set. Nine supervised machine learning algorithms were employed on a total weighted sample of 44,123 women and evaluated using performance metrics using Python version 3.11 statistical software. This study also employed the most popular outlines of Yufeng Guo’s steps of supervised machine learning and SHAP analysis to predict and identify important predictors of home delivery after ANC visits in East Africa. Results: Home delivery after ANC visit was highest in Malawi, Uganda, and Kenya. Among the nine machine learning algorithms random forest was fitted for this study. The Beeswarm plot of SHAP analysis showed that being a rural resident of women and having a second trimester of ANC visit increases the likelihood of home delivery after an ANC visit. Whereas rich household income, secondary educational level of husband, contraceptive use, short birth interval, primary educational level of husband, having no problems of distance to health facility, and having above four ANC visits decreased women’s home delivery after ANC visits in East Africa. Conclusion: The random forest machine learning classification algorithms effectively predict home delivery after the ANC visit. As a result, this study recommends, considering the top ten determinants of home delivery and guaranteeing high-quality health institution services from a qualified practitioner, women should begin antenatal care services early and often throughout their pregnancies. Developing health facilities, promoting media health education, and encouraging women to get adequate information on health care services. Moreover, healthcare policy should give great consideration to women from low-income households.

show abstract

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

Cited by 14 publications

References 67 publications

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia

Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

Machine learning algorithm to predict determinants of home delivery after ANC visit among reproductive age women in East Africa: Using SHAP

Contact Info

Product

Resources

About