Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
This study investigates the potential of multimodal data integration, which
combines electroencephalogram (EEG) data with sociodemographic characteristics
like age, sex, education, and intelligence quotient (IQ), to diagnose mental
diseases like schizophrenia, depression, and anxiety. Using Apache Spark and
convolutional neural networks (CNNs), a data-driven classification pipeline has
been developed for big data environment to effectively analyze massive
datasets. In order to evaluate brain activity and connection patterns
associated with mental disorders, EEG parameters such as power spectral density
(PSD) and coherence are examined. The importance of coherence features is
highlighted by comparative analysis, which shows significant improvement in
classification accuracy and robustness. This study emphasizes the significance
of holistic approaches for efficient diagnostic tools by integrating a variety
of data sources. The findings open the door for creative, data-driven
approaches to treating psychiatric diseases by demonstrating the potential of
utilizing big data, sophisticated deep learning methods, and multimodal
datasets to enhance the precision, usability, and comprehension of mental
health diagnostics.
