Investigation and Development of an Interpretable Machine Learning Model in Therapeutic Applications by Providing Solutions to Change the Condition of Patients, M.Sc. Thesis Sharif University of Technology ; Haji, Alireza (Supervisor)
Abstract
Despite the significant progress of machine learning models in the health domain, current advanced methods usually produce non-transparent and black-box models, and for this reason, they are not widely used in medical decision-making. To address the issue of non-transparency in black-box models, interpretable machine learning models have been developed. In the health domain, counterfactual scenarios can provide personalized explanations for predictions and suggest necessary changes to transition from an undesirable outcome class to a desirable one for physicians. The aim of this study is to present an interpretable machine learning framework in the health domain that, in addition to having...
Cataloging briefInvestigation and Development of an Interpretable Machine Learning Model in Therapeutic Applications by Providing Solutions to Change the Condition of Patients, M.Sc. Thesis Sharif University of Technology ; Haji, Alireza (Supervisor)
Abstract
Despite the significant progress of machine learning models in the health domain, current advanced methods usually produce non-transparent and black-box models, and for this reason, they are not widely used in medical decision-making. To address the issue of non-transparency in black-box models, interpretable machine learning models have been developed. In the health domain, counterfactual scenarios can provide personalized explanations for predictions and suggest necessary changes to transition from an undesirable outcome class to a desirable one for physicians. The aim of this study is to present an interpretable machine learning framework in the health domain that, in addition to having...
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