Abstract: Chronic Kidney Syndrome (CKD) is a persistent medical condition characterized by the gradual deterioration of renal function over time. It is a significant global health concern, impacting a substantial number of individuals. With advancements in technology, particularly in the field of machine learning (ML), there is an opportunity to utilize these tools for improving the detection, prediction and supervision of CKD. The objective of this scheme is to develop a extrapolative prototype for CKD and facilitate its management through the application of ML algorithms and techniques. By analyzing extensive datasets comprising patient medical records, demographic statistics, test site outcomes, and other relevant factors, this initiative aims to identify patterns, trends, and threat aspects accompanying with CKD. These insights can assist healthcare professionals in making more accurate assessments regarding CKD progression and devising personalized treatment plans. We propose the consumption of the Support Vector Machine (SVM) machine learning model to forecast CKD based on relevant clinical features. Our findings validate the effectiveness of the SVM model in accurately predicting CKD, achieving an impressive accuracy rate of 94%.
Keywords: chronic kidney infection; CKD stage identification; machine learning, support vector machine
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DOI:
10.17148/IJARCCE.2023.12704
