SHAP-Based Explainable Clustering for Medical Records Insights
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, leveragi...
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| Autores principales: | , , , |
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| Formato: | documento de conferencia conferenceObject acceptedVersion |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Nacional del Comahue. Facultad de Informática
2025
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| Acceso en línea: | https://rdi.uncoma.edu.ar/handle/uncomaid/18633 |
| Aporte de: |
| Sumario: | 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. |
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