Predicting depression in Bangladeshi undergraduates using machine learning

Abstract

Depression is a major disorder and a growing problem that impacts a person's way of living and disrupts natural functioning. Depression is especially prevalent in the younger population of underdeveloped and developing countries. Youth in countries such as Bangladesh face difficulties with studies, jobs, relationships, drugs, family problems which are all major or minor contributors in a pathway to depression. This research besides predicting depression in university undergraduates for the purpose of recommendation to a psychiatrist focuses on gaining valuable insights as to why university students of Bangladesh, undergraduates, in particular suffer from depression. The data for this research was collected by a survey designed after consultation with psychologists, counselors and professors. The best method for predicting depression among Bangladesh undergraduates was found out after using three algorithms to train and test the dataset. Random Forest was found to be the best algorithm, closely followed by Support Vector Machine with similar accuracy and f-measure of around 75% and 60%} respectively but Random Forest giving a better precision, recall and lower false negatives. The objective of this research is to check whether depression can be successfully predicted with the help of related features. This research aims to identify depression in its early stages and ensure a fast recovery for victims so that heartbreaking incidents like suicide can be avoided.