Major depressive disorder detection in polytechnic college students

Abstract

Over 322 million people worldwide are affected by depression, the leading cause of disability. Major depressive disorder (MDD) is the most frequent mental disorder and contributes significantly to the global disease burden. Current depression diagnoses, on the other hand, are beset by issues such as patient denial, clinical experience, and self-report bias. Early detection of depression can assist in lessening or even eliminating its detrimental effects. To help the conventional diagnostic approaches in psychiatry, there has been much research into automated depression prediction in recent years. Automated depression identification based on machine learning techniques can help disorder analysts diagnose depression more effectively. The literature on depression has proven that the acoustic features of a depressed individual vary from a normal individual. This paper aims to identify the minimal acoustic features that can be used to detect depression accurately and propose a majority voting classifier for detecting depression (MVCDD). MVCDD is designed with the base classifiers, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and Logistic Regression (LR). The experiments are performed on the data collected from the students of Government Polytechnic, Masabtank, Hyderabad, India.