Date of Award

5-2019

Document Type

Campus Access Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Marc Pomplun

Second Advisor

Dan A. Simovici

Third Advisor

Tran Tran

Abstract

In recent years, machine learning has gained huge traction. Researchers have continuously applied machine learning to new fields to unravel the complex patterns within huge and complex datasets. Machine learning approaches have been successfully applied in the field of computer vision to learn from any existing patterns in images and classify previously “unseen” images. Recently, machine learning has been used in the medical field in the context of disease diagnosis. In this study, we will use machine learning to distinguish healthy subjects from those suffering from schizophrenia. Schizophrenia affects the neurotransmitters of the brain, thus impairing the neural connectivity of the patient, resulting in various psychotic symptoms. It not only affects the patient, but there is a certain risk that their relatives might develop this disease as well. Symptoms of schizophrenia can be controlled with early detection of the disease. We use eye-movement recording to obtain measures for distinguishing schizophrenic patients from healthy controls using a deep neural network, which could assist in early detection of the disease and thereby make a difference to the lives of millions of people.

Comments

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