Research Article Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times P. M. Durai Raj Vincent , 1 Nivedhitha Mahendran , 1 Jamel Nebhen , 2 N. Deepa , 1 Kathiravan Srinivasan , 1 and Yuh-Chung Hu 3 1 School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632 014, Tamil Nadu, India 2 Prince Sattam Bin Abdulaziz University, College of Computer Engineering and Sciences, P.O. Box: 151, Alkharj 11942, Saudi Arabia 3 Department of Mechanical and Electromechanical Engineering, National ILan University, Shenlung Road, Yilan City 26047, Taiwan Correspondence should be addressed to Yuh-Chung Hu; ychu@niu.edu.tw Received 11 March 2021; Revised 26 March 2021; Accepted 9 April 2021; Published 27 April 2021 Academic Editor: Navid Razmjooy Copyright©2021P.M.DuraiRajVincentetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals’ lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whethertheyaredepressedornot.Depression,mostofthetime,cooccurswithanxietyandleadstosuicideinfewcases,amongthe employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. is is why this work aims to analyze the ITemployees who are mostly working with targets. e artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. is study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from ITprofessionals. is study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. e results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification. 1. Introduction In the present day pandemic scenario, where people always complain about stress, pressure, and anxiety, major de- pressive disorder is commonly seen as a leading mental disorder across the globe. When someone appears to have intense feelings such as sadness and distress for a consid- erable period, they might have major depressive disorder [1]. It has high impacts on mental and physical activities to the onesufferingfromit;also,thereisahigherriskofsuicide[2]. ose who have been suffering from MDD tend to feel uninterested in doing the activities they enjoyed doing once. Also, it affects their moods and behavior and finds difficulty in doing day-to-day activities. Most of those who die by killing themselves are found to have mental disorders that are treatable, mostly only due to depression they are doing so. e suicide rate is said to be around 15% among de- pressed people [3]. Major depressive disorder is a treatable mental disorder that appears when the individual is too stressed out because of various reasons of one’s life including hormonal changes [4]. Major depressive disorder is termed as comorbid [5], that is, a medical condition that tends to occur, and it is a tedious task to identify whether the individual is suffering Hindawi Computational Intelligence and Neuroscience Volume 2021, Article ID 9950332, 12 pages https://doi.org/10.1155/2021/9950332