Comparation analysis of naïve bayes and decision tree C4.5 for caesarean section prediction
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Abstract
The development of technology can be used to facilitate many matters. One of them is childbirth in the medical fields. Maternal mortality rate (MMR) is the number of maternal deaths during pregnancy to postpartum caused by pregnancy, childbirth or its management. There are several methods of labors that can be done. The determination of the labor is based on many factors and must be in accordance with the conditions of pregnant patient. Caesarean birth is the last alternative in labor, due to high risk factors. The objective of this research is to predicte and analyse caesarean section using C4.5 and Naïve Bayes classifier models. For experimentation the dataset is collected from UCI Machine Learning Repository and the main attributes represented in this dataset are age, delivery number, delivery time, blood of pressure, and heart problem. The accuracy using C4.5 by 80 training cases is 45% And the accuracy using Naïve Bayes is 50%.
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