2026-09-20T05:00:52Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1626922025-09-19T04:49:07Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Szolovits, Peter
Li, Jonathan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-09-18T14:27:36Z
2025-09-18T14:27:36Z
2025-05
2025-06-23T14:02:50.246Z
https://hdl.handle.net/1721.1/162692
This work focuses on the progression from metabolic dysfunction-associated fatty liver to metabolic dysfunction-associated steatohepatitis, a more serious prognosis that can lead to liver failure and death. Additional adverse progressed outcomes include hepatic failure, fibrosis, cirrhosis, and malignant neoplasm of liver and intrahepatic bile ducts. We explore the possibility of using different machine learning techniques, including logistic regression, XGBoost, random forest, and decision trees to predict the likelihood of progression. We use data from Massachusetts General Brigham to train our models, incorporating demographics, physical measurements, lab results, and doctor notes. As a result of this project, we our best model was an XGBoost classifier with an AUROC of 0.800 with random forest at a similar performance of 0.786. However, all of our models had low AUPRC and sensitivity, indicating both overfitting and an imbalanced dataset.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Predicting Progression of Metabolic Dysfunction-associated Steatotic Liver Disease
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>This work focuses on the progression from metabolic dysfunction-associated fatty liver to metabolic dysfunction-associated steatohepatitis, a more serious prognosis that can lead to liver failure and death. Additional adverse progressed outcomes include hepatic failure, fibrosis, cirrhosis, and malignant neoplasm of liver and intrahepatic bile ducts. We explore the possibility of using different machine learning techniques, including logistic regression, XGBoost, random forest, and decision trees to predict the likelihood of progression. We use data from Massachusetts General Brigham to train our models, incorporating demographics, physical measurements, lab results, and doctor notes. As a result of this project, we our best model was an XGBoost classifier with an AUROC of 0.800 with random forest at a similar performance of 0.786. However, all of our models had low AUPRC and sensitivity, indicating both overfitting and an imbalanced dataset.</Abstract>
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