Using Modified SBM and Decision Tree to Find Targets in Breast Cancer Data
Abstract
This study presents a combined approach using a modified Slacks-Based Measure (SBM) and a Decision Tree algorithm to identify target patients within breast cancer data. Traditional Data Envelopment Analysis (DEA) models often classify multiple patients as equally efficient, which limits the ability to distinguish between them. By modifying the SBM model to better handle input and output slacks, we aim to capture more accurate efficiency levels. We apply this method to the Scikit-learn breast cancer dataset, treating each patient as a Decision-Making Unit (DMU). The Decision Tree algorithm is used to identify the most significant features influencing efficiency. These key features are assigned higher weights in the SBM model to refine the analysis. The results allow for the identification of biologically significant target patients who demonstrate distinct efficiency profiles. This approach offers a useful tool for discovering hidden patterns in medical data and supports data-driven decision-making in cancer diagnosis and treatment planning.
Keywords:
Modified slacks-based measure model, Decision tree, Data envelopment analysis, Decision-making unit, Efficiency in cancer patientsReferences
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