Predicting Adverse Perinatal Outcomes among Women with Hypertensive Disorders of Pregnancy in Addis Ababa
| dc.contributor.advisor | Wondwossen Mulugeta | |
| dc.contributor.author | Rebecca Ashagire | |
| dc.date.accessioned | 2026-03-03T12:52:49Z | |
| dc.date.available | 2026-03-03T12:52:49Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Background - Hypertensive disorders of pregnancy (HDP) are among the leading causes of adverse perinatal outcomes (APOs) worldwide, with a particularly high burden in low- and middle- income countries (LMICs) like Ethiopia. Despite advancements in maternal healthcare, traditional risk assessment methods often fail to account for the complex nature of HDP, resulting in delayed interventions and preventable maternal and neonatal complications. The lack of efficient and accurate risk prediction tools in resource-limited settings contributes to high morbidity and mortality rates among pregnant women and newborns. Objective - This study aimed to develop a machine learning (ML) predictive model using electronic medical record (EMR) data to identify women with HDP who are at high risk for APOs. Methodology - This study applied a retrospective analysis of EMRs collected from selected public hospitals in Addis Ababa (AA), focusing on predicting APOs among pregnant women diagnosed with HDP. A total of 1042 instances was collected from four hospitals in AA. After performing data cleaning and preprocessing, including handling missing values using three imputation techniques (Median, KNN, and MICE), six supervised ML algorithms—Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and XGBoost—were developed. Model performance was evaluated using, internal cross-validation, two hyperparameter optimization strategies (Grid Search and Bayesian Optimization), and with metrics including accuracy, sensitivity, precision, F1-score, AUC-PR and ROC-AUC. Result - Random Forest and XGBoost models had the highest predictive performance, with XGBoost achieving the best overall results. However, even the best-performing model reached a maximum recall of only 69% for predicting the positive class, which indicates limitations in identifying APOs. Conclusion - The findings indicate that ML models show promise in predicting APOs among women with HDP using routinely collected clinical data. Most models achieved high specificity and ROC-AUC value, showing their potential for use in risk stratification. However, the recall performances were suboptimal for clinical application, which suggests the current models are not yet suitable for deployment. Further research with larger datasets, more diverse features, and broader validation is necessary. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/7812 | |
| dc.language.iso | en | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Predicting Adverse Perinatal Outcomes among Women | |
| dc.title | Predicting Adverse Perinatal Outcomes among Women with Hypertensive Disorders of Pregnancy in Addis Ababa | |
| dc.type | Thesis |