Naive Bayes classifier for infant weight prediction of hypertension mother

Abstract

Abstract Classification is one method of data analysis in data mining that is used to form a model in order to describe the appropriate data class or model that predicts data trends. The Usage of classification has been applied in various areas, including in health areas. One of the classification methods used is Naive Bayes. This study aims to predict the weight of infants in maternal hypertensive and nonhypertensive conditions with Naive Bayes method. Data were taken as many as 219 data from pregnant women based on the medical record in Obstetrics and Gynecology of Muhammadiyah Palembang Hospital from January 1017 until September 2017. Data is divided into two groups, 188 for training data and 31 data for testing data. The performance data analysis was using WEKA and the results showed that the Naive Bayes’s accuracy is 80.372%. the accuracy score means Naive Bayes works well to predict the weight of infants in maternal hypertensive and nonhypertensive mothers. The result is expected to be a reference for others research by comparing it with other classification methods and incorporating other factors in pregnancy and multiple births or other factors.