3.3. Classification and Detection of SZ through Other Techniques
This protocol is extracted from research article:
Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification
Int J Environ Res Public Health, Jun 5, 2021; DOI: 10.3390/ijerph18116099

The ways that genetic and DNA changes are related to SZ are not well understood, and the genetics of this disorder is an active area of research [135]. However, the benefit of using gene and protein data to classify SZ is the vast availability of data, which may propel the advancement of using ML techniques in this scope of research. There are also studies that aim to identify, classify and detect SZ through task-specific characteristics or non-neurological features through ML techniques. For example, cognitive and neuropsychological tests are used to examine whether neurological signs predict cognitive performance in SZ patients and to determine the ability of neurological signs and neuropsychological tests to discriminate SZ patients from healthy subjects [136,137,138,139,140]. Facial features is also an area of interest to detect SZ such as eye tracking [141] and facial features [142,143] as well as communication ability by tracking handwriting [144] and speech [145]. There are also traditional studies on brain shape and volume symmetry [146], signs of negative symptoms [147,148] and behavioural anomalies [149,150] as well as novel means of detecting by tracking keywords used on social media [151,152,153] or upbringing [154].

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