Classification of Maternal Emergencies Using Gaussian Naive Bayes to Speed up the Patient’s Triage Process

Introduction: labor is the most important process in every woman’s pregnancy. this requires optimal handling of various parties until labor takes place smoothly. the purpose of the study is to determine the triage classification of labor referral patients in hospitals using gaussian naive bayes as the final model. methods: this study used 90 data, each consisting of 15 parameters which are divided into two categorical data types: 9 data and 6 continuous data types. two treatments were used in this study, namely gaussian naive bayes (first) using the independence assumption on all parameters, and categorical naive bayes for categorical data types, and gaussian naive bayes for continuous data types. these two types of data were combined using gaussian naive bayes as the final model. the data went through a preprocessing stage, stratified cross-validation; then, we used the method of naive bayes according to the data type and continued for the final stage classification using gaussian naive bayes. results: the results of the first treatment had an accuracy of 91%, recall of 97%, precision of 64%, and f1-score of 73%. also, the second treatment had an accuracy of 96%, recall of 88%, precision of 86% and f1-score of 86%. the treatment of different data types had a difference in the final results compared to the treatment of the same data type. conclusion: the diversity of data types is best handled according to the model used.