Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions

Abstract

In maternity care, deciding which method to use sometimes depends on the interest of the mother. In other cases, the mode of delivery is decided based on the observed health condition of the mother and the fetus. Predicting mode of delivery before term would help reduce the excessive and insignificant usage of operative procedures. In this work, data mining classification models have been used to predict the mode of delivery in obstetrics by considering both maternal and fetal factors. Particularly, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Support Vector Machine, and Decision Trees models were used. The data used is a real dataset obtained from the maternity unit of Ahmadu Bello University Teaching Hospital, Zaria. All the used models were found to be efficient in predicting the mode of delivery as none has less than 90% accuracy. However, NB was found to be the best amongst all with an accuracy of 99.78% and KNN being the least but still with an accuracy of 91.41%. ARTICLE INFO