Chronic kidney disease prediction based on machine learning algorithms

Abstract

Chronic kidney disease (CKD) is a dangerous ailment that can last a person’s entire life and is caused by either kidney malignancy or decreased kidney function. It is feasible to halt or slow the progression of this chronic disease to an end-stage wherein dialysis or surgical intervention is the only method to preserve a patient’s life. Earlier detection and appropriate therapy can increase the likelihood of this happening. Throughout this research, the potential of several different machine learning approaches for providing an early diagnosis of CKD has been investigated. In recent years, machine learning (ML) algorithms have become a powerful tool in medical diagnosis, offering the potential to predict kidney disease with high accuracy. This paper investigates the application of various ML techniques, including Decision Trees, Support Vector Machines (SVM), Random Forests, Gradient Boost Classifier, Xgboost, KNN, and Logistic Regression, in predicting kidney disease using clinical data. Therefore, in our approach, we investigate the link that exists between data factors as well as the characteristics of the target class. We are capable of constructing a collection of prediction models with the help of machine learning and predictive analytics. The performance of these algorithms is evaluated based on metrics such as accuracy, sensitivity, specificity, and receiver operating characteristic (ROC).