Comparison of Sampling Methods Using Machine Learning and Deep Learning Algorithms with an Imbalanced Data Set for the Prevention of Violence Against Physicians

Abstract

The aim of this study is to compare sampling methods using machine and deep learning algorithms with a small and imbalanced data set for the prevention of violence against physicians. In this data set, it is determined whether there is violence against physicians by using various demographic information of physicians. In addition, in this study, it is tried find effective solutions to improve the working conditions of physicians in order to reduce violence against physicians. As a solution to the imbalanced data problem, Synthetic Minority Oversampling (SMOTE), Random Oversampling (ROS) and Random Undersampling (RUS) methods were used to balance the data in this study. Then, Random Forest Classifier (RFC), Extra Tree Classifier (ETC) and Multi-Layer Perceptron (MLP) algorithms were applied. Among all sampling techniques and classification algorithms, the ETC algorithm applied with the ROS method shows the best performance with 82% accuracy and 0.81 F1-Score.