Determining factors leading to depression using Machine Learning

Abstract

With most people leading a hectic lifestyle today, depression has become a prevalent ailment. In addition to stress, several other factors contribute to depression, such as hormonal imbalances, medications, rough childhood, etc. In the present times, there has been a surge in understanding mental health problems through social media as the dominant channel. Despite these efforts, many individuals are still unable to identify this illness's traits and fail to prevent it at the early stages. The therapy sessions conducted by experts are expensive, making it unaffordable for most of the population to reach out for treatment, reducing the reach of such interventions. There have been attempts to identify the factors leading to depression and its effects on human behaviour with the help of advanced machine learning techniques. These findings can significantly improve the diagnosis of these mental disorders and prove to be a breakthrough in the healthcare sector. This analysis can also help business sectors understand human behaviour in more depth, assisting them in managing their key stakeholders such as managers, employees, or even consumers in any organization.