  Schizophrenia is a severe yet treatable mental disorder, it is diagnosed
using a multitude of primary and secondary symptoms. Diagnosis and treatment
for each individual depends on the severity of the symptoms, therefore there is
a need for accurate, personalised assessments. However, the process can be both
time-consuming and subjective; hence, there is a motivation to explore
automated methods that can offer consistent diagnosis and precise symptom
assessments, thereby complementing the work of healthcare practitioners.
Machine Learning has demonstrated impressive capabilities across numerous
domains, including medicine; the use of Machine Learning in patient assessment
holds great promise for healthcare professionals and patients alike, as it can
lead to more consistent and accurate symptom estimation.This survey aims to
review methodologies that utilise Machine Learning for diagnosis and assessment
of schizophrenia. Contrary to previous reviews that primarily focused on binary
classification, this work recognises the complexity of the condition and
instead, offers an overview of Machine Learning methods designed for
fine-grained symptom estimation. We cover multiple modalities, namely Medical
Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can
manifest themselves both in a patient's pathology and behaviour. Finally, we
analyse the datasets and methodologies used in the studies and identify trends,
gaps as well as opportunities for future research.
