MENTAL DISORDER PREDICTION USING MACHINE LEARNING

The modern lifestyle has increased stress and pressure, and people are facing psychological problems and anomalies. However, the usefulness of these abnormalities in distinguishing individual patients with bipolar disorder from mood disorders or healthy controls and stratifying patients by overall disease and burden has not been evaluated in large cohorts. Is not … This study uses a machine learning approach to study bipolar disorder using the Mood Disorder Questionnaire (MDQ). A dataset is fed to a decision tree classifier, which determines the most important features in the dataset and makes them the decision factors for that level in the decision tree. Test samples are compared to each level of determinant, and this determination classifies each test case into a defined class (screened positive or screened negative). The Mood Disorder Questionnaire is a viable method for detecting bipolar disorder.