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- W4310257708 abstract "Chronic kidney disease (CKD) is a global scientific issue marked by extreme gloom and a high death rate, and it causes extraordinary illness. Because there aren't any obvious side effects in the early stages of CKD, patients frequently fail to monitor their condition. A persistent renal problem is difficult to diagnose. Due to their quick and precise acknowledgment execution, machine learning of fashions can successfully assist scientific achieve this. As a result, we recommend using a logistic regression tool to diagnose chronic renal disease. The professional gadget compares the collection of rules, which includes ANN, C4.5, Support vector system set of rules, and KNN, as well as fuzzy policies, and provides an accuracy of 98.75. The feature selection and classification is used, and produce the ultimate result includes accuracy, f-measure, and recall. Our proposed system’s primary objective is to predict the disease provided with a fuzzy rules along with machine learning model and find the accuracy of the disease in an early stages. Results shows that proposed technique withstands data processing and fuzzy sets to evaluate the accuracy. As a result, we speculated that the way of thinking could be appropriate for locating further confusing medical data." @default.
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- W4310257708 date "2022-11-25" @default.
- W4310257708 modified "2023-10-18" @default.
- W4310257708 title "Support System for Chronic Kidney Disease Prediction Using Fuzzy Logic and Feature Selection" @default.
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- W4310257708 doi "https://doi.org/10.1007/978-981-19-5292-0_41" @default.
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