PATIENT CLASSIFICATION USING DEEP LEARNING

With diseases like Alzheimer's and Influenza still claiming lives, there have been a lot of methods developed in order to combat these diseases. There is a possibility that the key to finding susceptibility towards a disease might lie in the patient's genetic makeup. The purpose of this thesis is to see if it is possible to predict whether a person is likely to suffer from a certain disease based on gene expression values. In order to achieve this goal, a computational based approach was adopted. Currently, artificial intelligence is producing results that were deemed not possible a few years ago. Moreover, deep learning, one specific branch of artificial intelligence, has been used to produce useful results. It has been used in many new technologies such as self-driving cars, natural language processing, and many other automated systems. This research came up with a method that makes use of a deep learning approach and found that it is indeed effective in classifying patients.

Level thesis:degree_level

Master's Degree

Year dc:date.issued

2019

Author dc:creator

• Shrestha, Sangam

Advisor dc:contributor.advisor

• Nguyen, Tin

Committee members dc:contributor.committeemember

• Harris, Jr., Frederick C.
• Guragai, Binod

• Alzheimer's
• Classification
• Deep Learning
• Early Diagnosis
• Influenza
• machine learning

Handle dc:identifier.uri

http://hdl.handle.net/11714/6718

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/6718