Unveiling Diagnostic Clarity: A Machine Learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics

This research work provides a comprehensive overview of the similarities and differences between borderline personality disorder (BPD) and bipolar disorder, and sheds light on how they are often considered comparable due to overlapping symptoms. Previous studies on this problem are reviewed and emphasize the need for a robust classifier to improve diagnostic accuracy. The goal of this study is to create a machine learning (ML) system that uses a random forest to effectively distinguish between borderline personality disorder (BPD) and bipolar disorder. Disorder, two commonly misdiagnosed psychiatric disorders. BPD and bipolar disorder share common features such as mood instability and impulsivity, which can lead to diagnostic problems and misdiagnosis. The machine learning method is built with a Random Forest, which is known for its general learning ability and feature importance estimation. The Random Forest model can effectively distinguish between BPD and bipolar patients using a wide range of clinical and demographic data, thereby reducing diagnostic confusion. The proposed approach can be a useful clinical tool to help doctors make a more accurate and faster diagnosis. This research will improve mental health diagnosis, address an important clinical problem and ultimately support better treatments for people with borderline personality disorder (BPD) and bipolar disorder.