Prediction of Mental Health Treatment Adherence using Machine Learning Algorithms

Mental health is an important part of a person’s total well-being. Identifying individuals who require medical intervention for mental health disorders, on the other hand, can be difficult, leading to delayed or insufficient treatment. The goal of this research is to create a prediction model that can reliably determine whether a person needs medical treatment for mental health issues. The model will be built with a mix of demographic and clinical data, as well as self-reported symptoms, acquired from a variety of sources, including medical records, survey results, and online health groups. This study’ dataset will be compiled from a range of sources, including medical records, survey results, and online health forums. Cross-validation techniques will be used to validate the model, and its correctness will be measured using standard assessment criteria. The ultimate purpose of this project is to provide a tool that healthcare practitioners may utilize to better the detection and treatment of mental health disorders.