Abstract : Chronic Kidney Disease (CKD) is one of the deadliest diseases that slowly damages human kidney. The disease remains undetected in its early stage and the patients can only realize the severity of the disease when it gets advanced. Hence, detecting such disease at earlier stage is a key challenge now. Machine Learning is one of the emerging field used in the health sectors for the diagnosis of different diseases. In this paper, we compute, analyze and compare between Machine Learning classification approaches to determine which classification approach is the optimal for the prediction of CKD. Random Forest Algorithm and Logistic Regression are some renowned machine learning methods which were selected to train the model and based on these results, we can compare and determine which among the following Machine Learning Methods can predict the possibility of CKD at the most accurate level. From this comparative analysis, Random Forest Algorithm is found to be the best approach to predict CKD. Methods can predict the possibility of CKD at the most accurate level. From this comparative analysis, Random Forest Algorithm is found to be the best approach to predict CKD.
Keywords: Machine Learning, Classification Technique, Prediction System
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DOI:
10.17148/IJARCCE.2022.11643
