Why the study?
Chronic kidney disease is difficult to detect early due to non-prominent symptoms, and early prediction of AKI and CKD with machine learning could improve diagnosis and reduce treatment costs.
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An intelligent decision support system using machine learning was designed to predict the occurrence and severity of acute kidney injury and chronic kidney disease.
Clinical judgements can be improved by the use of artificial intelligence (AI) in the routine examinations. In case of chronic kidney diseases (CKD), it is quite difficult to detect at the early stages and afterwards the patient’s condition worsens very quickly. This is only because of the non-prominent disease specific symptoms at the early stages. An early prediction of AKI and CKD with machine learning can be a key to diagnose and reduces the cost of treatment. By using medical data mining of renal patients an intelligent decision support system (DSS) is designed using MATLAB environment, which enables the user to predict the various condition with maximum accuracy of prediction; whether the disease occurs or not and if yes then what is its severity.
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Sharma et al. (2021) studied this question.
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