Authors: Professor Dr. Satya Singh, Ratnesh Kumar Sharma
Abstract: COVID-19 harmed the lives of people in every region of the world. It has been established that, in addition to the physical symptoms, it significantly influences the patient’s mental health. Depression has been identified as one of the most widespread disorders that can hasten a person’s mortality at an early age. This is one of the conditions that has been singled out for this distinction. The trajectory of life for millions of people has been altered as a result of this illness. We conducted a survey that consisted of 21 questions based on the Hamilton instrument and the advice of a psychiatrist. This was done so that we could continue forward with the inquiry into the identification of depression in individuals. After the data were compiled and analysed, it became clear that people younger than 45 years of age had a higher risk of suffering from depression when compared to those older than 45 years of age. This is because most people at this age are concerned about getting married or schooling their children. On the other side, research has revealed that those whose ages fall between 18 and 25 are also at an increased risk of suffering from depression. This is likely because, at this stage in their lives, these individuals are more conscious of the potential outcomes of their lives. Based on all of the replies received, the findings of the survey were put through several different machine learning algorithms, including Decision Tree, KNN, and Naive Bayes. These algorithms were used to analyse the results. Further investigation is being done into how these two techniques are similar to and different from one another. According to the findings of the research, KNN has produced better results than other approaches in terms of accuracy, whereas decision trees have produced better results in terms of the amount of time needed to detect depression in a person. In conclusion, to overcome the traditional approach to a depression diagnosis, which is made up of affirmative questions and constant feedback from individuals, a model that is based on machine learning is offered as a potential alternative.
DOI: https://doi.org/10.5281/zenodo.15804697
