Title: Machine learning to predict unintended pregnancy among reproductive-age women in
Ethiopia: evidence from EDHS 2016
Version: 3 Date: 20 Dec 2023
Reviewer's report:
The paper addresses the pressing issue of unintended pregnancies in Ethiopia, utilizing machine
learning algorithms to predict and identify key determinants. The study employs data from the
2016 Ethiopian Demographic and Health Survey (EDHS) and assesses the effectiveness of six
machine learning algorithms, ultimately selecting the ExtraTrees classifier as the top-performing
model. The identified predictors include region, ideal number of children, religion, wealth index,
age at first sex, husband education, refusal of sex, total births, age at first birth, and mother's
educational status. The study highlights the potential of machine learning in improving policy
decision-making and reducing the societal and economic burden of unintended pregnancies.
Review:
Introduction and Significance:
The introduction effectively sets the stage, highlighting the significance of unintended
pregnancies and the associated societal costs.
The global and local context is well-established, emphasizing the relevance of the study in
Ethiopia.
Methodology:
The methodology is thorough and detailed, providing clarity on data sources, processing, and
analysis techniques.
The use of machine learning algorithms, especially the ExtraTrees classifier, is justified and
aligns with the study's objectives.
The inclusion of feature importance analysis and performance evaluation metrics enhances the
rigor of the methodology.
Results:
The presentation of sociodemographic characteristics provides a comprehensive overview of
the study population.
Imbalanced data handling using SMOTE is appropriately justified, and the comparative analysis
of balanced and unbalanced datasets strengthens the study's robustness.
The results section effectively communicates the performance of various machine learning
algorithms, with ExtraTrees standing out as the top performer.
Discussion:
The discussion provides insightful interpretations of the results, linking them to existing
literature and offering plausible explanations for identified predictors.
The exploration of regional disparities, wealth index, and religious influences on unintended
pregnancies enriches the discussion.
Conclusion:
The conclusion effectively summarizes the key findings, emphasizing the potential of the
ExtraTrees classifier in predicting unintended pregnancies.
The discussion on limitations adds transparency to the study, acknowledging the challenges
associated with machine learning models.
Recommendations:
The paper concludes with a call for the use of machine learning algorithms in shaping policy
decisions to address unintended pregnancies effectively.
The recommendations align with the study's objectives and contribute to the broader discourse
on public health interventions.
Overall Structure and Clarity:
The paper follows a logical structure, with clear delineation of sections.
Figures and tables are appropriately used to enhance understanding.
The writing style is generally clear and concise, contributing to the overall readability of the
paper.
Potential Improvements:
While the abstract is informative, it could be more concise in summarizing key findings and
their implications.
The paper could benefit from a more explicit connection between the identified predictors and
the potential interventions or policy implications.
Future Directions:
It would be valuable for the authors to discuss potential future research directions, such as
validating the model on a different dataset or exploring the implementation of suggested policy
changes.
In conclusion, the paper provides a valuable contribution to the field of public health by
leveraging machine learning to predict unintended pregnancies in Ethiopia. The methodology,
results, and discussion are well-executed, and the study has the potential to inform policy
decisions and interventions aimed at reducing the prevalence of unintended pregnancies.