Shannon Entropy Analysis of Serum C-Terminal Agrin Fragment as a Biomarker for Kidney Function: Reference Ranges, Healing Sequences and Insights
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
1 - Department of Mathematics, Adyaman University, Adyaman, T¨urkiye.
کلید واژه: Healing sequence, Shannon entropy, Fuzzy set, Renal transplant, Biomarker.,
چکیده مقاله :
This article focuses on evaluating the success or failure of kidney transplantation using Shannon entropy, fuzzy sets, and Scaf. The data for Scaf references used in this study for both healthy individuals and kidney transplant recipients have been collected from the relevant literature. For both groups, Scaf's Shannon entropy values have been calculated using an appropriate probability density function and formulation, and sequences have been generated for CAF and Scr biomarkers from entropy values, with findings interpreted. These sequences are called healing sequences. A case study demonstrating whether the transplant procedure was successful or unsuccessful was presented using sequences that we refer to as healing sequences. In this context, the utilization of mathematical tools such as fuzzy sets, Shannon entropy, and reference intervals becomes evident. These tools provide a systematic and quantitative approach to assessing the outcomes of kidney transplantation. By leveraging the principles of Shannon entropy, we gain insights into the degree of unpredictability and fuzziness associated with biomarker values, which can be indicative of the transplant's success. Furthermore, the concept of healing sequences provides a valuable framework for tracking the progression of patients post-transplantation. By monitoring changes in CAF and Scr biomarkers over time, healthcare professionals can make informed decisions and interventions to ensure the well-being of kidney transplant recipients.
This article focuses on evaluating the success or failure of kidney transplantation using Shannon entropy, fuzzy sets, and Scaf. The data for Scaf references used in this study for both healthy individuals and kidney transplant recipients have been collected from the relevant literature. For both groups, Scaf's Shannon entropy values have been calculated using an appropriate probability density function and formulation, and sequences have been generated for CAF and Scr biomarkers from entropy values, with findings interpreted. These sequences are called healing sequences. A case study demonstrating whether the transplant procedure was successful or unsuccessful was presented using sequences that we refer to as healing sequences. In this context, the utilization of mathematical tools such as fuzzy sets, Shannon entropy, and reference intervals becomes evident. These tools provide a systematic and quantitative approach to assessing the outcomes of kidney transplantation. By leveraging the principles of Shannon entropy, we gain insights into the degree of unpredictability and fuzziness associated with biomarker values, which can be indicative of the transplant's success. Furthermore, the concept of healing sequences provides a valuable framework for tracking the progression of patients post-transplantation. By monitoring changes in CAF and Scr biomarkers over time, healthcare professionals can make informed decisions and interventions to ensure the well-being of kidney transplant recipients.
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