Abstract
Bipolar disorder, a complex disorder in brain has affected many
millions of people around the world. This brain disorder is identified
by the occurrence of the oscillations of the patient’s changing mood.
The mood swing between two states i.e. depression and mania. This is a
result of different psychological and physical features. A set of
psycholinguistic features like behavioral changes, mood swings and
mental illness are observed to provide feedback on health and wellness.
The study is an objective measure of identifying the stress level of
human brain that could improve the harmful effects associated with it
considerably. In the paper, we present the study prediction of symptoms
and behavior of a commonly known mental health illness, bipolar disorder
using Machine Learning Techniques. Therefore, we extracted data from
articles and research papers were studied and analyzed by using
statistical analysis tools and machine learning (ML) techniques. Data is
visualized to extract and communicate meaningful information from
complex datasets on predicting and optimizing various day to day
analyses. The study also includes the various research papers having
machine Learning algorithms and different classifiers like Decision
Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic
Regression and K- Nearest Neighbor are studied and analyzed for
identifying the mental state in a target group. The purpose of the paper
is mainly to explore the challenges, adequacy and limitations in
detecting the mental health condition using Machine Learning Techniques