Diagnosis of neurological and psychiatric diseases based on whole-brain functional connectivity using Machine Learning techniques
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Date
2022
Authors
Tatosh, Sofiia
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Abstract
The global problem this thesis aims to target is the inability of psychotherapists and
psychiatrists always correctly to identify a presence of a mental illness. To give a
constructive medical conclusion on a patient’s state, usually, it is not enough to only
rely on symptoms concluded from a therapeutic session. Moreover, the diagnosis
of that kind could be biased from both a therapist and a patient’s side. The former
depends on the doctor’s knowledge and experience, and the latter is based on an
ability to communicate the mental state. Notably, the more researchers investigate
the cause of psychiatric diseases, the more they make sure that mental illnesses are
developed due to specific changes in one’s brain. It could be the brain’s structure,
functionality, or damage, leading to changes in a person’s behavior, thought process,
interaction with other people, and sometimes difficulties in functioning as a healthy
human being. It is believed that severe mental illnesses and neurological and developmental
diseases result from abnormal connectivity in a brain network. That
is why whole-brain functional connectivity is a significant source of information in
this study. In simple words, it represents if and how the brain regions communicate
with each other. This study presents generalized, usable, and reliable classification
models to identify a specific neurological or psychiatric disease, Autism Spectrum
Disorder and Schizophrenia, with 92.4% and 93.8% of accuracy respectfully, for further
clinical application of the developed tool.
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Citation
Tatosh Sofiia. Diagnosis of neurological and psychiatric diseases based on whole-brain functional connectivity using Machine Learning techniques. Bachelor Thesis. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2022, 52 p.