Abstract:
"Depression is a mental condition in which the majority of mankind is affected simply due to sudden
altering of mood of humans. Most dangerous effects of depression is Suicide, while Covid-19 had
taken part as the most common reason for arising of depression for people who work from home.
However due to depression, the companies often suffer loss in productivity. In addition, loss of
productivity can lead to impairment of maintaining the company's reputation. The main reason for
companies suffering huge losses is due to the failure in acknowledging the state of depressed
employees. Furthermore, certain research had depicted about depression through the emotions of
employees and their risk of productivity loss towards the company by using a new scale. In this new
scale taking the emotions about sleep routine, low mood, lack of energy, hopelessness, and lack of
pleasure.
Company employees can login to web application called Pro {Test} and give answers as text base
inputs for the relevant questions according to the new scale and easily can find depressed level
percentage. After that can calculate productivity loss to the company by the depression level of that
employee by giving company per hour productivity and lost working hours of the employee. Then,
the result displays the company productivity loss how many percentage forced the depression. The
development of this present web application aided via Supervised machine learning. The machine
learning model of the web application gives prediction accuracy as 85 % for the Support Vector
Machine algorithm.
The testing of the application done under two categories as Non-Functional testing and Functional
testing. In functional testing, it conducted through the functional requirements of the research. In
non-functional testing has done through the several areas. The implemented machine learning
model accuracy tested under accuracy testing, and it contributed to check the model prediction
output is similar to the expected output. The performance testing and security testing helped to
understand the system performance and security of the system. Therefore, the system accurate the
best performance and accuracy."
