Date of Award
1-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
School of Computing
Thesis Sponsor/Dissertation Chair/Project Chair
Michelle M. Zhu
Committee Member
Jiayin Wang
Committee Member
Hongbo Zhou
Abstract
Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting decision-making processes and contribute to the execution of treatment strategies. This study utilized a comprehensive dataset including demographic information, clinical characteristics, and treatment outcomes of lung cancer patients. Used techniques enabled the evaluation, analysis, and modeling of lung cancer data with high accuracy. This approach enabled the evaluation, analysis, and modeling of lung cancer data with high accuracy. In this study, Random Forest demonstrated the highest performance among the tested models for determining lung cancer severity levels, followed by K-Nearest Neighbors (KNN). The findings offer in-depth analyses to enhance lung cancer treatment protocols and improve clinical decision-making. The results will contribute to the advancement of early treatment and the improvement of patients' life expectancy.
File Format
Recommended Citation
Bilgin, Esin, "Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced by Statistical Analysis" (2025). Theses, Dissertations and Culminating Projects. 1454.
https://digitalcommons.montclair.edu/etd/1454