Detection of bipolar disorder by means of ensemble machine learning classifier

Abstract

The accurate diagnosis of bipolar disorder is extremely challenging, due to unpredictable mood swings, behaviors, sleep, judgment, and inability to think, which makes it difficult to make a proper diagnosis. This paper aims to investigate the application of ensemble classifiers in classifying bipolar disorder and to compare their performance with existing methods. Herein, the work involves a thorough analysis of diagnostic precision and performance metrics. According to a study, an existing classifier achieved an accuracy rate of 87% in bipolar disorder classification. In addition, the two most widely used classifiers, which are Random Forest and Decision Tree, achieved accuracy rates of 90% and 86%, respectively. These results highlight the performance baseline against which the proposed ensemble classifier is evaluated. Notably, the proposed ensemble classifier shows excellent results in bipolar disorder classification thereby, achieving an impressive accuracy rate of 98%. This considerable improvement in accuracy marks a significant stride in diagnostic precision, showcasing the potential of ensemble classifiers in enhancing bipolar disorder detection. The results of this study have given substantial implications for the field of mental health diagnosis, offering a promising avenue for a more accurate and reliable classification of bipolar disorder. This research reinforces the significance of advanced machine learning techniques and their potential to revolutionize the approach to diagnose and to manage mental health conditions.