Mental health predictive models for triaging young adults

Femi Isiaq, Lawrence Dawson

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review

Abstract

Mental health is a state of well-being in which an individual realises own abilities and can productively cope with the stresses of life. Unfortunately, issues surrounding the mental health of young adults span every socio-economic group in the world. Such include a lack of access to adequate medical service and associated stigma among other factors. In recent times, various studies have indicated computer applications are increasingly contributing to the management of human well-being and other life activities. Subsequently, machine learning models have proved effective in predicting future activities and occurrences. This work involves the development of three models, which aim to establish a benchmark for mental health disorders prediction. The recorded results are promising with AUC scores of 96% (anxiety) and 93% (depression). This work provides the groundwork around the deployment of machine learning models for the development of computer applications that can improve the prediction of common mental health disorders, namely anxiety and depression, hence, it could be upscaled from a controlled environment to real-world application.
Original languageEnglish
Title of host publication2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
PublisherIEEE
Pages1-6
DOIs
Publication statusPublished - 30 Dec 2022

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