Predicting the Utilization of Mental Health Treatment with Various Machine Learning Algorithms

— In 2017, about 792 million people (more than 10% of the global population) lived their lives with a mental disorder [24]– 78 million of which committed suicide because of it. In these unprecedented times of COVID-19, mental health challenges have been even further exacerbated as home environments have been proven to be major sources of the creation and worsening of poor mental health. Additionally, proper diagnosis and treatment for people with mental health disorders remains underdeveloped in modern-day’s society due to the widely ever-present public stigma attached to caring about mental health. Recently there have been attempts in the data science world to predict if a person is suicidal (and other diagnostic approaches) yet all face major setbacks. To begin, big data has many ethical issues related to privacy and reusability without permission—especially in regards to using feeds from social media. Additionally, people diagnosed with specific mental health conditions may not actually seek treatment, so data may be incorrect. In this research, we address both of these problems by using anonymous datasets to predict the answer to a different question—whether or not people are seeking mental health treatment. We also use a large variety of machine learning and deep learning classifiers and predictive models to predict with a high accuracy rate through statistical analysis.