Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
Journal:
arXiv
Published Date:
Dec 9, 2024
Abstract
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.
