With the rise in mental health challenges over the years, especially in student populations, the need to develop targeted diagnostic and treatment tools has increased. Given the substantial research to highlight the tremendous variation in mental health symptomology, efforts to develop intervention strategies may benefit from insights into the individual. In this study, we investigate the predictive capabilities of random forest classifiers (RFC) in prediction of mental health risk scores in mental health data from the Westborough High School. Our results show variation in mental health outcomes across different student populations and underscore the need for personalized interventions over a one-size-fits-all approach.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Lo, Wesley

Subject

• Health
• Well-Being

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042623-221357
• 105651

Keyword

• Treatment
• Mental Health
• Machine Learning
• Depression
• Predictive Analysis
• Anxiety

Advisor

• Korkin, Dmitry
• Rodriguez, Angela C Incollingo
• Nephew, Benjamin

Year

• 2023

Date created

• 2023-04-26

Resource type

• Major Qualifying Project

Major

• Applied Mathematics

Source

• E-project-042623-221357

Rights statement

• In Copyright

Last modified

• 2023-06-21