Kidney Disease Prediction using Support Vector Machine and Random Forest Machine Learning
Raghavendra Patil G EAishwarya B BhiradiT S ChandanaH AshwiniImpana S Nayak
Abstract
Chronic kidney disease (CKD) known as chronic renal disease is the situation where kidney loose their ability to filter the blood as they should. Early prediction and appropriate treatment can slow or stop down the progression of this CKD. Machine learning algorithms are an important aid for health care professionals to make accurate diagnosis in the early stages of this illness. In order to predict CKD, this study suggests using machine learning algorithms like Support Vector Machine (SVM) and Random Forest (RF). The final output uses minimum count of characteristics to predict if people have CKD or not.
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