A Study on Borderline Personality Disorder using Machine Learning and Conventional Methods

Day by day, health issues are increasing because of our busy life. In health issues mental illness is one of the significant problems in the youth. So, there is a need to study mentally disturbed people to find the solution to the problem. The main goal of this study is to find a refining technique to improve the performance of machine learning-based methods in data of mentally disturbed people. In particular, machine learning techniques must almost have excellent outcomes in borderline personality disorder to receive the right therapy as quickly as possible. In this research, we analyzed the facts for the likelihood that a person in a depressed mood will be referred to the borderline personality disorder. The basic idea explained in the manuscript will focus on understanding the significance, importance, and potential applications of ML in defining objectives bases outcomes not only for borderline personality disorders but for many other ailments in mentally ill patients. This depth analysis will help address many issues that can help understand the depression patterns in individuals, mood fluctuations in depressed people, and related topics.