Predictive Modelling of Bipolar Disorder Utilizing Advanced Machine Learning Techniques

A complex mental health disease called bipolar disorder is characterised by recurrent manic and depressive episodes. For effective treatment planning and management, bipolar illness must be predicted accurately and promptly. Recently, machine learning approaches have popularity in the healthcare industry and have exhibited encouraging results in the diagnosis of certain medical conditions. This paper offers a thorough examination of machine learning methods for bipolar disorder prediction modelling. This study's objective is to look at the potential of cutting-edge machine learning models for more accurate and reliable bipolar disorder prediction. Numerous machine learning techniques, which are not restricted to decision trees, support vector machines, random forests, logistic regression, and neural networks are utilised to develop prediction models. The models are trained and evaluated using a sizable and representative dataset that includes characteristics of those with bipolar disorder, both clinically and demographically.