Polarize-AI - Ensemble Machine Learning for Early Diagnosis of Bipolar Disorder through Social Media Analysis

Abstract

"Bipolar disorder is a complex mental illness that poses significant challenges for accurate diagnosis, particularly in its early stages as there are presently no reliable and effective diagnostic techniques available. Limited attention has been placed on the potential advantages of ensemble machine learning techniques for this purpose, despite previous research exploring the use of conventional machine learning algorithms to detect signs of bipolar disorder in social media data. This work suggests an ensemble machine learning technique to identify people with bipolar illness using Twitter data to address this knowledge gap in past literature. The author gathered a sizable dataset of tweets from people with and without bipolar disorder, and then utilized a variety of data preparation approaches to identify linguistic and behavioral traits linked to the illness. To increase the precision and caliber of bipolar illness detection, the retrieved characteristics were utilized to create an ensemble ML model, which integrates multiple distinct ML methods. To attain the best performance, the model was adjusted utilizing hyperparameter tuning methods."