Exploring Bipolar Disorder and Academic Performance with Hybrid Resnet-LSTM Model

The neurological condition termed bipolar disorder is characterized by significant mood swings and intense mood swings. It is currently the most common abnormality with a connection with mental health, and people of every age fail to recognize it once again. In general, bipolar disorder occurs in families, but not every one of the siblings will be affected by its risk variables. In this instance, we combine the information from MRI (magnetic resonance imaging) using the random forest technique. These variations serve the purpose of identifying particular bipolar disorder patients from individuals with psychological conditions. Medical and Research data indicates that delay in treatment results in significant levels of stress and mood swing events with more serious consequences like cardiac arrest, brain stroke, heart attack and depression. The study focuses on identifying the mental level through analyzing and choosing features, categorizing and validating by cross-validation. To identify bipolar disorder at an early stage, the present study proposes a machine learning (ML)-based framework. The proposed structure is also helpful in designing a computer-based tool for recognizing stress and mental health predictions. This research examines the different transformations like depression to Mania, from depression to BD, from BD to hypomania, and to identify pre-bipolar depression: logistic regression, Support Vector Machine, and another machine. Learning Regression and k-nearest-neighbour analysis are used to analyze risk variables and output accuracy.