  Machine learning is employed in healthcare to draw approximate conclusions
regarding human diseases and mental health problems. Compared to older
traditional methods, it can help to analyze data more efficiently and produce
better and more dependable results. Millions of people are affected by
schizophrenia, which is a chronic mental disorder that can significantly impact
their lives. Many machine learning algorithms have been developed to predict
and prevent this disease, and they can potentially be implemented in the
diagnosis of individuals who have it. This survey aims to review papers that
have focused on the use of deep learning to detect and predict schizophrenia
using EEG signals, functional magnetic resonance imaging (fMRI), and diffusion
magnetic resonance imaging (dMRI). With our chosen search strategy, we assessed
ten publications from 2019 to 2022. All studies achieved successful predictions
of more than 80%. This review provides summaries of the studies and compares
their notable aspects. In the field of artificial intelligence (AI) and machine
learning (ML) for schizophrenia, significant advances have been made due to the
availability of ML tools, and we are optimistic that this field will continue
to grow.

Neurons and Cognition;Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing