A Efficient Method to Predicting Chronic Kidney Disease using Machine Learning

chronic kidney disease (CKD), commonly referred to as chronic renal disease, is a group of disorders that affect your kidneys and reduce their ability to keep you healthy. Nerve harm, high blood pressure, anemic (a low blood count), weak bones, and inadequate nutrition could be symptoms. Early detection and treatment often prevents chronic renal disease from getting worse. Data mining is the technique used to gather knowledge from huge datasets. Data mining, which aims to use previous data to find recurring patterns and enhance decision-making moving forward, is a result of the convergence of several current trends: the decreasing price of massive data storage devices and the growing simplicity of data collection across networks; the development of reliable and effective machine learning algorithms to handle this data, as well as the declining cost of computing power, which makes it possible to apply computationally expensive techniques for data analysis. Machine learning has already produced useful applications in fields like analyzing results from medical research, spotting fraud, spotting bogus users, etc. For the purpose of predicting chronic diseases, various data mining and categorization methodologies and machine learning algorithms are used. The goal of this study is to develop a new decision-support system for forecasting chronic renal disease. This study compares support vector machine (SVM) classifier performance for CKD prediction based on its accuracy, precision, and execution time