Classification and Diagnosis of Renal Failure Disease Using Machines Learning
DOI:
https://doi.org/10.62933/qgj6hr87Abstract
During the past few years, the number of classification techniques has increased with the rapid growth of technology, which depends on machine learning. Recently, medical experts and doctors have widely utilized machine learning, a branch of artificial intelligence, to aid in predicting and diagnosing various diseases. Regarding health research, machine learning techniques are used extensively for data processing. In this study, we applied three different machine learning algorithms to a medical diagnosis problem and analyzed their efficiency in predicting the results. The study focuses on the diagnosis and factors influencing renal failure disease, using a serum test for both presence and absence patients. The dataset used for the study consists of 165 cases and 13 attributes of RFD patients. The goal of this study is to find out how well k-nearest neighbors (KNN), decision tree (DT), and random forest (RF) classifiers work by looking at things like accuracy, geometric mean, kappa coefficient and area under the curve for RFD prediction. The experimental results show that the RF classifier performs better than the other classifiers. Additionally, based on the final fitted models, it was found that urea, albumin, and magnesium are the most significant factors that clearly impact patients with renal failure disease.
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Copyright (c) 2025 Ammar Ahmed Ali, Omar Q. Alshebly, Rizgar Maghdid Ahmed (Author)
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