Machine Learning Prediction of Preterm Birth: An Analysis of Facility- Based Paper Health Records in Uganda
Loading...
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Research Square
Abstract
Preterm Birth (PTB) is one of the leading causes of neonatal mortality in Uganda. Machine Learning (ML) can be used to identify
women at risk of PTB in time for medical intervention and adequate preparation by mothers.
Methods: We utilized data from paper-based maternal health records at Kawempe National Referral Hospital, Uganda. A case-control
method was employed, where for every woman who experienced a PTB, a woman without PTB and delivered in the same day was selected
as a control. Treatment of missing data was done using Random Forest imputation. Variable Importance was analyzed using Random
Forest. The following classification methods were applied in the prediction of PTB: Logistic Regression (LR), Random Forest (RF), Decision
Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB). Performance of methods was investigated
using prediction accuracy, sensitivity, and specificity.
Results: 1,540 women were included in the study, where 770 women had experienced PTB, and 770 women formed the controls. According
to variable importance analysis, number of antenatal care visits had the biggest impact on PTB. SVM had the highest accuracy in predicting
PTB at 64% (sensitivity 64% and sensitivity 63%).
Conclusions: Prediction of PTB using paper-based records in a developing country yielded similar results to studies done using electronic
health records in developed countries. The predictive power could be low in this study due to fewer variables available from routinely
collected ANC data. The inclusion of significant variables in the maternal records could potentially increase predictive power.
Description
Keywords
Preterm Birth, Machine Learning, Variable Importance, Paper Medical Records
Citation
Memon, S., Wamala, R., & Kabano, I. (2022). Machine Learning Prediction of Preterm Birth: An Analysis of Facility-Based Paper Health Records in Uganda. https://doi.org/10.21203/rs.3.rs-1877209/v1