Integrating Fuzzy Logic and Medical Parameters for Reliable Chronic Kidney Disease Risk Prediction

Authors

  • Dr. Shaitan Singh Assistant Professor, Department of Mathematics, Veerbhumi Government PG College, Mahoba (UP), India
  • Dr. Deepak Patidar Assistant Professor Medicaps University Indore (MP), India
  • Dr. Ashish Kumar Soni Assistant Professor Medicaps University Indore (MP), India
  • Dr. Pradeep Kashyap Assistant Professor, Department of Mathematics, Shri Sadguru Saibaba Science & Commerce College, Ashti (Maharashtra), India
  • Dr. A.K. Singh Assistant Professor, Department of Applied Sciences, RBS Engineering Technical Campus, Agra (UP), India

Keywords:

Fuzzy logic models, chronic kidney disease (CKD), risk prediction, creatinine levels, blood urea nitrogen (BUN), protein in urine, kidney health, fuzzy inference system,

Abstract

This study explores the integration of fuzzy logic with critical medical parameters-Creatinine, Blood Urea Nitrogen (BUN), and Protein in Urine—to enhance the accuracy and reliability of Chronic Kidney Disease (CKD) risk prediction. Traditional diagnostic methods often struggle with the inherent uncertainty and variability in medical data, leading to potential misclassifications. By employing a fuzzy logic-based model, this research systematically incorporates varying levels of the three biomarkers to infer CKD risk levels as Low, Moderate, or High. The fuzzy rule base allows for nuanced decision-making that aligns more closely with clinical observations, providing a more flexible and adaptive approach to risk assessment. This model aims to support healthcare professionals in early diagnosis and improved patient management by offering a transparent, interpretable framework for CKD risk evaluation amidst uncertain data conditions.

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Published

2024-09-07

How to Cite

Dr. Shaitan Singh, Dr. Deepak Patidar, Dr. Ashish Kumar Soni, Dr. Pradeep Kashyap, & Dr. A.K. Singh. (2024). Integrating Fuzzy Logic and Medical Parameters for Reliable Chronic Kidney Disease Risk Prediction. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 678–685. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1412

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