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
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Olmedo Rivera, Juan Cristobal, "Assessing the impact of Principal Component Analysis on accurately predicting melanoma diagnosis applied on different classification models" (2019). Industrial, Manufacturing, and Systems Theses. 15.
https://mavmatrix.uta.edu/industrialmanusys_theses/15
Comments
Degree granted by The University of Texas at Arlington