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."
