Predicting the Use Labor Induction Intervention: A Machine Learning Approach for the Birth-Cohort Registry at A Tertiary Hospital in North Tanzania.

Abstract

Abstract Introduction : Following an increased use of labor induction procedure to prevent adverse maternal and fetal outcomes in Sub-Saharan Africa, hitting the best algorithm that accurately classify subjects in need of the intervention is of paramount importance. This study aimed at comparing the potential benefits of applying machine learning (ML) algorithms over the conventional logistic regression model in predicting the use of labor induction intervention in pregnant women attending one of the tertiary hospitals in north Tanzania for delivery. Methods : We conducted a secondary data analysis of the Kilimanjaro Christian Medical Centre (KCMC) birth registry database for women with uncomplicated pregnancies from the year 2000 to 2015. We excluded observations with non-vertex presentation and those with missing information on labor induction status. Model accuracy and Area under the receiver operating characteristic curve (AUC - ROC) were used to assess the discriminative ability of the selected models. We plotted the decision curve analysis (DCA) to assess the clinical utility of the models under observation. Results : A total of 21,578 deliveries were analyzed. Among these, 8814 (41%) were induced during the study period. Among the selected machine learning models, Random forest algorithm exhibited the best performance in terms of accuracy [0.75; 95%CI (0.73 – 0.76)] and AUC-ROC [AUC-ROC: 0.75; 95% CI (0.74 – 0.76)] compared to other models including logistic regression. Among assessed maternal attributes, parity, maternal age, body mass index, gestational age and birthweight were deemed most important predictors for labor induction intervention. Conclusion : The selected machine learning methods offered better computational performance compared to the conventional logistic regression model in predicting the use of labor induction intervention. The current study lends substantial support to the use of machine learning models in predicting the use of labor induction intervention.

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