SHAP-Based Explainable Clustering for Medical Records Insights

Abstract

Machine Learning is a fundamental tool for information analysis. Among its various techniques, clustering stands out as a family of algorithms capable of dividing large datasets into distinct groups based on similarity. In the healthcare domain, state-of-the-art research has been conducted, leveraging the vast availability of patient medical data, which makes clustering a powerful tool for knowledge discovery. However, Machine Learning also presents limitations, such as difficulties in explaining its results and the potential for unethical biases, which pose significant challenges for real-world applications. This study explores opportunities for applying clustering techniques within the Social Security Insurance system of Universidad Nacional del Comahue, a university located in Neuquén, Argentina. Additionally, clustering will be combined with the SHAP framework to enhance the explainability of the obtained results.