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Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females

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Date Issued:
2021
Abstract/Description:
One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.
Title: Efficient Machine Learning Algorithms for Identifying Risk Factors of Prostate and Breast Cancers among Males and Females.
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Name(s): Rikhtehgaran, Samaneh , 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: 49 p.
Language(s): English
Abstract/Description: One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide. The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.
Identifier: FA00013755 (IID)
Degree granted: Thesis (P.S.M.)--Florida Atlantic University, 2021.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Machine learning
Algorithms
Cancer--Risk factors
Breast--Cancer
Prostate--Cancer
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013755
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.