Integrated Rnn-Based Rvm Model to Detect Bipolar Disorder

Abstract

This research introduces a novel integrated model that combines Recurrent Neural Networks (RNN) with the Relevance Vector Machine (RVM) to resolve key challenges in mental health diagnosis. The proposed RNN-based RVM (RR) model advances traditional methods by enhancing the accuracy of bipolar disorder detection, thereby supporting early intervention and improved clinical outcomes. The objectives are to leverage RNN for large-scale data analysis and RVM for robust classification, to validate performance across diverse datasets. By applying the integrated model to three different types of datasets, the study demonstrates its potential to enable earlier diagnosis, more effective treatments and reduced diagnostic burden in Bipolar Disorder (BD) detection. The findings are expected to provide valuable insights into the role of advanced machine learning techniques in enhancing BD diagnostics.