Review of Class Imbalance Dataset Handling Techniques for Depression Prediction and Detection

Abstract

Depression is a prevailing mental disturbance affecting an individual’s thinking and mental development. There have been much research demonstrating effective automated prediction and detection of Depression. Many datasets used suffer from class imbalance where samples of a dominant class outnumber the minority class that is to be detected. This review paper uses the PRISMA review methodology to enlist different class imbalance handling techniques used in Depression prediction and detection research. The articles were taken from information technology databases. The research gap was found that under sampling methods were few for predicting and detecting Depression and regression modelling could be considered for future research. The results also revealed that the common data level technique is SMOTE as a single method and the common ensemble method is SMOTE, oversampling and under sampling techniques. The model level consisted of various algorithms that can be used to tackle the class imbalance problem.