Author Type

Graduate Student

Date of Award

2021

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Muhammad, Wazir

Abstract

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.

Extent

49 p.

Comments

Includes bibliography.|Thesis (P.S.M.)--Florida Atlantic University, 2021.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013755

Format

pdf

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