Abstract:
Mental health problems are becoming the order of the day and burdening the traditional psychiatric
guidance frameworks in terms of expenses, unreachability and waiting durations. Conversely,
mental health chatbots that are based on AI have become popular because of their
anonymity, 24/7, and low cost. Although both traditional counseling and chatbot approaches
have feasible advantages, no standard way of operating has been established to compare the
efficacy of the two with individual patients. This has been a barrier to the use of individualized
mental health interventions. The paper examines the application of machine learning and in this
case, the Random Forest algorithm to predict and compare the results of conventional therapy
and AI chatbot assistance. Available references define the major signs of treatment success and
provide an overview of the benefits and shortcomings of chatbot interventions, yet no model
exists to evaluate how people can react to the alternative medium. To solve this, a random forest
model was created using data on clinical therapy outcome and the results were used on the data
of chatbot users to forecast possible outcomes. The reported chatbot outcomes were compared
statistically and through the qualitative feedback with the expected therapy outcomes. The research
will establish personal characteristics that relate to increased benefits in either form of
therapy. The expected outcomes will be used in clinical decision making, enhancement of digital
mental health tools and help in choosing the most appropriate treatment.
