Implementation of the CBR (Case Based Reasoning) Method in Cases of Cesarean section

Abstract

CBR (Case Based Reasoning) method is a reasoning method that uses old knowledge to overcome new problems. CBR will provide solutions to new cases by looking at old cases that are closest to new cases. One case that can use the CBR method is a case of cesarean section because there are several factors that affect cesarean section as well as features in the system, including: age, number of pregnancies, time of delivery, blood pressure, and heart status so that not everyone can do surgery cesar. In this study a system was used to determine whether a patient could have a cesarean section or not by using the CBR method and calculate similarity using Naive Bayes. The percentage correlation value of each feature is sought using SPSS because each feature has a different effect on the results. The number of cesarean section data was 80 data, in this study were divided into 70% training data (56) and 30% testing data (24). Where the new case data will be compared with the old case data in the database, and then the similarity criteria are calculated based on the existing formula. The results of testing of 24 data testing there are 5 data whose results are incompatible and 19 data whose results are in accordance with the data before it is shared. So that the accuracy of the cesarean section with the CBR method using Nayve Bayes is 79%.