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Revolutionizing Early Diagnosis on a Multifaceted Approach to Chronic Kidney Disease Detection
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Author(s): Naveen Kumar Pareek (Sangam University, India), Deepika Soni (Sangam University, India)and Awanit Kumar (Sangam University, India)
Copyright: 2024
Pages: 19
Source title:
Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch023
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Abstract
A growing number of people throughout the world are suffering from chronic kidney disease (CKD), which is a major public health issue. Detection and prediction of CKD are crucial for healthcare providers to intervene timely and effectively in the fight against the disease. A number of medical fields have seen encouraging results from combining AI technologies with fuzzy logic and expert systems in recent years. The purpose of this study is to develop a CKD prediction model using an expert system that combines AI and fuzzy logic. By combining nephrologists' extensive knowledge with fuzzy logic and AI algorithms, the suggested expert system can improve prediction accuracy. A number of clinical and laboratory variables are integrated into the system. These include age, blood pressure, serum creatinine, and urine protein levels, among others. Fuzzy logic takes into account the inherent imprecision and ambiguity of medical data.
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