A HYBRID METHOD FOR DETECTION OF KIDNEY DISEASE USING MACHINE LEARNING
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Abstract
The decline of kidney function is known as Chronic Kidney Disease (CKD). Known also as chronic renal disease, chronic kidney disease is an abnormal functioning of the kidneys or a progressive loss of renal function that develops over months or years. is a significant burden on the healthcare system because to the rising patient population, poor prognosis for morbidity and death, and increased risk of developing end-stage renal disease. yearly because of the illness. Therefore, it takes time, the illness's symptoms are not immediately apparent, and a large number of lives are lost before kidney disease is taken seriously in its early stages. Modern Machine Learning (ML) approaches are being used to detect several key health hazards, including the prediction of diabetes, the detection of brain tumors, the identification of COVID-19, the detection of renal illnesses, and many more. Therefore, the illness may be predicted in this study utilizing these machine learning classifiers, hybrid namely KNN and Logistic Regression. Our major goal is to distinguish between different machine learning algorithms based on their correctness in terms of performance. This hybrid method can produce accurate and dependable F1- score and accuracy outcomes.
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