Category: Review

SHAP Values: A New Frontier in Secondary Analysis Research

Thomas Freeman In secondary analysis research, traditional statistical models like logistic and linear regression have long been the standard tools for deriving insights from pre-existing datasets. These models, while foundational, often struggle to manage the complexities of modern datasets, including high-dimensionality, non-linear relationships, and interactions among variables. Machine learning (ML) methods, with their ability to…

Review

Application of SHAP Values in Secondary Data Analysis and Medical Research

Introduction In recent years, the use of machine learning (ML) in medical research has expanded significantly, offering new ways to analyze complex datasets. However, the interpretability of these models remains a challenge. SHAP (SHapley Additive exPlanations) values have emerged as a powerful tool for enhancing model interpretability by assigning an importance value to each feature…

Review