A comparative study of machine learning techniques for chronic kidney disease prediction.

A major global health issue, chronic kidney disease affects millions of individuals. Machine learning algorithms have showed promisein predicting the risk of developing disease, and early detection of chronic kidney disease is essential to preventing or slowing down its course. Machine learning techniques for chronic kidney disease prediction are investigated. Also, the dataset was subjected to the appropriate feature selection technique. The wrapper technique, feature selection, and complete features were used, respectively, to calculate the output of each classifier. Logistic regression classifier, KNN, random forest classifier, Ada Boost, Gradient Boosting, Stochastic Gradient Boosting (SGB), Extra Trees Classifier, and LGBM Classifier are a few of the techniques and models that are examined for chronic kidney disease prediction. Extra Trees Classifier, LGBM Classifier are discussed. Additionally, different features and datasets used in chronic kidney disease prediction are analyzed. Finally, the performance of various machine learning models is evaluated, and future directions for chronic kidney disease prediction research are outlined. Overall, Machine learning algorithms have the potential to significantly improve early detection and management of CKD, thus reducing the burden of this disease on healthcare systems and individuals. [ABSTRACT FROM AUTHOR]