Date of Award

August 2021

Degree Type

Thesis

Degree Name

Master of Science

Department

Computer Science

First Advisor

Tian Zhao

Second Advisor

Jake Luo

Committee Members

Cristine Cheng

Keywords

Deep Learning, Lung cancer, Machine Learning Pipeline, Supervised Machine Learning, Top features influencing lung cancer

Abstract

Lung cancer is the leading cause of cancer-related death around the world. Early detection is a critical factor for its effective treatment. To facilitate early-stage prediction, a Machine Learning (ML) pipeline has been built that uses inpatient admission data to train four ML models. The data is dynamically loaded into a database, cleaned, and passed through the SelectKBest selector to identify the top features influencing the prognosis, which are then fed into the pipeline and fitted to the ML models to make the forecast. Among the models used, Decision Tree provides the highest accuracy (97.09%), followed by Random Forest (94.07%). MLP and Logistic Regression reach an accuracy of 84.58% and 77.65% respectively. Some of the top 50 features include chronic obstructive pulmonary disease, pleural effusion, secondary and unspecified malignant neoplasm of intrathoracic lymph nodes, syndrome of inappropriate secretion of antidiuretic hormone, and neoplasm-related acute, chronic pain.

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