Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

Abstract

Objective: We aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.

Design: A secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.

Setting: The KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.

Participants: Singleton deliveries (n=42 319) with complete records from 2000 to 2015.

Primary outcome measures: Perinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.

Results: The proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)-over the logistic regression model across a range of threshold probability values.

Conclusions: In this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

Keywords: epidemiology; neonatology; perinatology; prenatal diagnosis; reproductive medicine.

Figure 1

Schematic diagram showing the number of singleton deliveries analysed, KCMC medical birth registry data, 2000–2015. KCMC, Kilimanjaro Christian Medical Centre.

Figure 2

Trends of perinatal death, KCMC medical birth registry data, 2000–2015. KCMC, Kilimanjaro Christian Medical Centre.

Figure 3

Variable importance of predictors for perinatal death in the random forest model scaled to have a maximum value of 100. ANC, antenatal care.

Figure 4

Prediction ability of perinatal deaths comparing different machine learning models in the test set: (A) Receiver operating characteristics curves. The corresponding values of the area under the receiver operating characteristics curve for each model are in table 2. (B) Decision curve analysis. The net benefit of the machine learning models (except for boosting) is larger over a range of threshold probability values compared with that of the logistic regression model.