Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
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arXiv
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| Format: | Preprint |
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2024
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| author | Kannan, Kamala Devi Jagatheesaperumal, Senthil Kumar Kandala, Rajesh N. V. P. S. Lotfaliany, Mojtaba Alizadehsanid, Roohallah Mohebbi, Mohammadreza |
| author_facet | Kannan, Kamala Devi Jagatheesaperumal, Senthil Kumar Kandala, Rajesh N. V. P. S. Lotfaliany, Mojtaba Alizadehsanid, Roohallah Mohebbi, Mohammadreza |
| contents | For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder Kannan, Kamala Devi Jagatheesaperumal, Senthil Kumar Kandala, Rajesh N. V. P. S. Lotfaliany, Mojtaba Alizadehsanid, Roohallah Mohebbi, Mohammadreza Machine Learning Emerging Technologies 68T05, 92C50, 62H30 I.2.6; I.2.7; H.5.2; H.2.8 For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services. |
| title | Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder |
| topic | Machine Learning Emerging Technologies 68T05, 92C50, 62H30 I.2.6; I.2.7; H.5.2; H.2.8 |
| url | https://arxiv.org/abs/2412.06147 |