Analysis of Major Depressive Disorder Using Ensemble Technique

Abstract

Depression is a medical condition that exists on continuum of severity ranging from mild, momentary states of low mood to severe, long term symptoms that have a major impact on an individual’s life. It is one of the primary causes of disability worldwide and contributes significantly to the global illness burden. The complexity for analyzing the major depressive disorder can be implemented using mental health dataset acquired from the World Health Organization. Ensemble machine learning aims to supervise on the strength and weakness of commonly used machine learning models, in order to minimize the risk in decision making. The experimental outcomes reveal that our ensemble technique used to analyze the depression brought out high acceptability in term of prediction and an accuracy of 95% was achieved.