Navigating The Complexities Of Machine Learning In Mental Health Prognosis: Paradigms And Challenges

Abstract

Mental disorders are an increasing worldwide problem, and traditional ways of diagnosing mental health are often restricted by subjectivity and use of retrospective information. The problem discussed in this paper is the lack of accuracy and generalizability of existing mental health prognosis systems. The overall goal is to review the opportunities and obstacles of implementing Machine Learning (ML) methods in mental health prediction and treatment optimization. The literature-based approach was used, whereby the research reviewed literature published in 2015-2022 to determine the data, model, implementation, and ethical challenges. Results indicate that challenges related to poor data quality, limited diversity, model bias, issues to do with interpretability and absence of real-world validation impede successful deployment. Findings reveal that although, ML algorithms such as SVM, random forest, and deep learning models have potential, their applicability to clinical practice is limited. The study finds that there is a need to have strong, interpretable, and ethically viable ML strategies. The future looks promising now with the use of deep learning, multimodal data, transfer learning, and federated learning to deploy scalable, accurate and patient-centred mental health solutions.