Using Machine Learning to Diagnose Depression: A Review of Research Trends and Algorithms

Mr. Geofrey Mwamba Nyabuto and Dr. Alice Nambiro
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Mr. Geofrey Mwamba Nyabuto: Kibabii University, Information Technology, P.O. Box 1699—50200, Bungoma County
Dr. Alice Nambiro: Kibabii University, Information Technology, P.O. Box 1699—50200, Bungoma County

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 2, 84-94

Abstract: Depression is one of the most common mental health illnesses that affects millions of people worldwide. In the recent past, there have been numerous studies on depression and mental health at large. Artificial Intelligence (AI) technologies have increasingly been adopted to solve complex human-related problems including the diagnosis and treatment of different diseases. Researchers have been trying to establish if and how Machine Learning (ML), a branch of AI, can be used to diagnose depression to help solve this complex problem of mental health. In this paper, we seek to review and establish unique research trends in using ML algorithms to diagnose depression as well as establish the most recommended ML algorithm that has shown high accuracy and hence mostly been recommended by many research articles. The study consisted of 2 phases where in the first phase, 3 major publishers and journals were considered to establish research trends. The phase considered articles published between 2015 to 2023. In the second phase, a total of 20 journal articles with open access and having been published between 2020 and 2023 were considered to establish the most recommended ML algorithm for solving the problem of diagnosing depression among people.

Date: 2024
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