Classification Of Maternal Health Risk Using Three Models Naive Bayes Method

Abstract

Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.