Improved Depression System Using Neuro-Fuzzy Inference System (Journal of Science and Technology, 2025)
The place of depression in impeding organisational productivity cannot be overemphasised, as this silent and almost neglected mental health disorder has caused loss of revenue, loss of jobs, death, and risk of suicide in the workplace. Depression is a prevalent and dangerous medical condition that has an adverse effect on one's emotions, thoughts, and behaviour. Some of the basic symptoms of depression include sadness and/or a loss of interest in once-enjoyed activities, and it can impair the capacity of an individual to perform both at work and at home and result in a range of emotional and physical issues. With an estimated 6.7%, which accounts for one in fifteen adults suffering from depression in any given year. Furthermore, 16.6% of people, or one in six, will at some point in their lives experience depression; therefore, the need for a system to accurately detect the disease in a timely manner. This paper, therefore, proposes the development of a workplace depression detection system using a mobile application development platform. The proposed system was developed in Android Studio with Java and Kotlin using the object-orientated analysis and design (OOAD) methodology, which is a flexible software development paradigm that takes an object-orientated approach. The new mobile apps have great potential in addressing the burden of workplace depression with the aim to intervene with at-risk people, particularly in the workplace, which is crucial to their effectiveness, as the system shows high accuracy in identifying depression, and it is portable, secure, and user-friendly, providing an amazing user experience (UX). The system can be implemented and used in the workplace, making it accessible to all and thereby enhancing employee efficiency and productivity.