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Liver Cancer Risk Quantification through an Artificial Neural Network based on Personal Health Data

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Date Issued:
2021
Summary:
Liver cancer is the sixth most common type of cancer worldwide and is the third leading cause of cancer related mortality. Several types of cancer can form in the liver. Hepatocellular carcinoma (HCC) makes up 75%-85% of all primary liver cancers and it is a malignant disease with limited therapeutic options due to its aggressive progression. While the exact cause of liver cancer may not be known, habits/lifestyle may increase the risk of developing the disease. Several risk prediction models for HCC are available for individuals with hepatitis B and C virus infections who are at high risk but not for general population. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data to predict liver cancer risk. Our results indicate that our ANN can be used to predict liver cancer risk with changes with lifestyle and may provide a novel approach to identify patients at higher risk and can be bene ted from early diagnosis.
Title: Liver Cancer Risk Quantification through an Artificial Neural Network based on Personal Health Data.
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Name(s): Ataei, Afrouz, author
Muhammad, Wazir, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Physics
Charles E. Schmidt College of Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2021
Date Issued: 2021
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 38 p.
Language(s): English
Summary: Liver cancer is the sixth most common type of cancer worldwide and is the third leading cause of cancer related mortality. Several types of cancer can form in the liver. Hepatocellular carcinoma (HCC) makes up 75%-85% of all primary liver cancers and it is a malignant disease with limited therapeutic options due to its aggressive progression. While the exact cause of liver cancer may not be known, habits/lifestyle may increase the risk of developing the disease. Several risk prediction models for HCC are available for individuals with hepatitis B and C virus infections who are at high risk but not for general population. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data to predict liver cancer risk. Our results indicate that our ANN can be used to predict liver cancer risk with changes with lifestyle and may provide a novel approach to identify patients at higher risk and can be bene ted from early diagnosis.
Identifier: FA00013742 (IID)
Degree granted: Thesis (PMS)--Florida Atlantic University, 2021.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Liver--Cancer
Artificial neural networks
Neural networks (Computer science)
Cancer--Risk assessment
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013742
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.