Feature Selection Based on Naive Bayes for Caesarean Section Prediction

Abstract

Data mining using machine learning algorithms can be used to help analyze historical data to predict the need for a caesarean section. The dataset used for predicting caesarean section has many features, but those features have the possibility of redundancy or irrelevance that can cause a decrease in classifier performance. This research proposes a model that implements feature selection to select relevant features and can provide improved performance predictions for caesarean section. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection (SBFS), Sequential Backward Floating Selection (SBFS), and selectKBest. The classification algorithm used to classify is Naive Bayes. The model that gives the best performance value is the model that applies the SelectKbest as feature selection.