Version 1
: Received: 17 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (10:48:52 CEST)
How to cite:
Tamoor, M.; Ahmed, H.; Saad, A.; Asaad, H.; Sulaiman, M.; mushtaq, M. Improving Mental Health with Machine Learning: Classification of Mental Disorders Using Local Data. Preprints2024, 2024081389. https://doi.org/10.20944/preprints202408.1389.v1
Tamoor, M.; Ahmed, H.; Saad, A.; Asaad, H.; Sulaiman, M.; mushtaq, M. Improving Mental Health with Machine Learning: Classification of Mental Disorders Using Local Data. Preprints 2024, 2024081389. https://doi.org/10.20944/preprints202408.1389.v1
Tamoor, M.; Ahmed, H.; Saad, A.; Asaad, H.; Sulaiman, M.; mushtaq, M. Improving Mental Health with Machine Learning: Classification of Mental Disorders Using Local Data. Preprints2024, 2024081389. https://doi.org/10.20944/preprints202408.1389.v1
APA Style
Tamoor, M., Ahmed, H., Saad, A., Asaad, H., Sulaiman, M., & mushtaq, M. (2024). Improving Mental Health with Machine Learning: Classification of Mental Disorders Using Local Data. Preprints. https://doi.org/10.20944/preprints202408.1389.v1
Chicago/Turabian Style
Tamoor, M., Muhammad Sulaiman and mubashar mushtaq. 2024 "Improving Mental Health with Machine Learning: Classification of Mental Disorders Using Local Data" Preprints. https://doi.org/10.20944/preprints202408.1389.v1
Abstract
Researchers are dedicated to providing effective solutions for serious problems such as mental health. This field of study ranges from understanding the impact of mental health ailments on individuals to identifying trends in mental health diseases based on various data points. Unfortunately, the journey from diagnosis to receiving a proper mental health regimen is costly and inaccessible to many. This necessitates the introduction of an affordable alternative that enables individuals with internet access to connect to a platform to determine whether they require the services of a mental health professional. Additionally, such a platform can assist mental health professionals in quickly identifying patients with mental health conditions. Our proposed solution leverages hidden trends in data obtained from 700 mental health patients at The local mental hospital in Pakistan. We have used this data to train a machine learning classifier capable of distinguishing between Psychotic and Neurotic disorders. Psychotic disorders are a group of mental health conditions characterized by a loss of touch with reality whereas neurotic disorders are a group of mental health conditions characterized by excessive worry, anxiety, or stress that does not involve a loss of touch with reality. Our model achieved an overall accuracy score of 73%, indicating successful prediction of the correct diagnosis in 73 out of 100 pipeline tests. While these results may not seem significant, an increase in data points would surely allow for further improvements in these results and the overall solution we propose.
Keywords
psychotic disorders; mental disorders; machine learning; health data analysis; neural networks
Subject
Computer Science and Mathematics, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
