ORCID Identifier(s)

0000-0002-4664-0586

Graduation Semester and Year

2019

Language

English

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Kim Aera LeBoulluec

Abstract

With huge amounts of data at our disposal in the medical field, mathematical models are built to diagnose diseases. This study focuses on melanoma because it’s the type of skin cancer that accounts for most deaths, up to 7,230 in 2019 according to the American Cancer Society. The study focuses on the effectiveness on diagnosing melanoma and how Principal Component Analysis (PCA) impacts the performance of four models being assessed, which are: K Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Each model evaluates the melanoma dataset before and after performing the PCA transformation. Results show that PCA does not impact performance in this case. Even though PCA does not improve performance, the modeled results achieve better results when compared to dermatologist and other algorithms.

Keywords

Melanoma, Principal component analysis, K nearest neighbor, Logistic regression, Support vector machines, Artificial neural networks

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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