The Artistry of Stochastic Gradient Boosting and Gradient Boosting Classifiers in Chronic Renal Disease Classification

Abstract

Chronic kidney failure is a medical disorder that impairs the kidneys' overall capacity to filter dangerous substances from your blood and maintain overall health. Anemia, weakened bones, poor diet, trauma, and decreased blood pressure are some of the factors that contribute to chronic kidney disease. Anemia, weakened bones, poor diet, trauma, and decreased blood pressure are some of the factors that contribute to chronic kidney disease. This study proposes using a several unsupervised algorithms, evaluate their performance, and determine the optimal combinations with higher accuracy. In addition to scaling features and applying the method to balance the data, this list of processes includes correctly imputed missing data points. The present study has employed six supervised algorithms, including DT, RF, SGB, XgBoost, Ada Boost and Gradient Boosting Classifiers has integrated them with techniques for selecting features based on the Pearson Correlation Coefficient. Classifying clinical data of chronic kidney diseases(CKD) and Non-CKD with an overall accuracy of 99.2% has been achieved by integrating feature reduction approaches with Stochastic Gradient Boosting and Gradient Boosting Classifier. These models were put to the test using a collection of data on chronic kidney illness from the University of California at Irvine, this had four hundred entries of data with twenty-six attributes. The outcomes of various models are examined. The model built with Stochastic Gradient Boosting and Gradient Boosting Classifier method performed the best in terms of correctness using 24 attributes for the small dataset, based on the comparison. The final phase of this study will examine how effectively the machine learning system predicts chronic kidney failure with respect to precision, recall, accuracy, & F1-Score.