Leveraging Machine Learning and Deep Learning to Enhance Lean Operations in Healthcare: A Focus on Lung Cancer Detection

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dc.contributor.advisorChen, Fengshan
dc.contributor.authorDe La Rosa, Kevin
dc.contributor.committeeMemberWan, Hung-Da
dc.contributor.committeeMemberAbbaas, Omar
dc.creator.orcidhttps://orcid.org/0009-0006-4702-1265
dc.date.accessioned2024-03-26T22:49:29Z
dc.date.available2024-03-26T22:49:29Z
dc.date.issued2023
dc.description.abstractAs cancer ranks within the top five causes of death within the United States, the current cancer environment, applications of lean methodologies in the healthcare industry, and the implementation of artificial intelligence and machine learning to support cancer treatment, patient's experiences, and oncology operations is explored. Statistical analysis is then performed on a lung cancer patient dataset to understand the correlation the variables have to cancer diagnosis. Various artificial intelligence models such as Random Forest (RF), Convolutional Neural Networks (CNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron Neural Network (MLP-NN) are then applied to the dataset to evaluate model accuracy and identify if the application can improve oncology centers operational and treatment efficiency. XGBoost with and without Principal Component Analysis (PCA), Logistic Regression, Random Forest, and MLP-NN with and without PCA achieved an accuracy of 100%, with LR with PCA (98.93%), and CNN (96.27%) following. These high accuracies confirm the implementation of artificial intelligence within the healthcare organization can be successful in supporting diagnosis predictions and enhancing lean operations.
dc.description.departmentMechanical Engineering
dc.format.extent1 electronic resource (56 pages)
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798381180770
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6257
dc.languageeng
dc.subjectArtificial Intelligence
dc.subjectComputational Pathology
dc.subjectHealthcare
dc.subjectLean Six Sigma
dc.subjectLung Cancer
dc.subjectOncology
dc.subject.classificationMechanical engineering
dc.subject.classificationOncology
dc.titleLeveraging Machine Learning and Deep Learning to Enhance Lean Operations in Healthcare: A Focus on Lung Cancer Detection
dc.typeThesis
dc.type.dcmiText
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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