Effective Classification of Chronic Kidney Disease Using Extreme Gradient Boosting Algorithm

Abstract

With a high rate of morbidity and mortality, chronic kidney disease is a global health issue that also causes other diseases. Patients frequently overlook the condition because there aren't any evident symptoms in the early stages of CKD. An efficient and effective Extreme gradient boosting method for the early diagnosis of kidney illness has been proposed in this paper to explore the capability of various machine learning algorithms. DenseNet can extract a variety of features such as vector features. After that feature extraction phase, the data are fed into the feature selection phase. The features are selected based upon the Improved Salp swarm Algorithm (ISSA). The proposed CKD classification method has been simulated in PYTHON. Utilizing the CKD dataset from the UCI machine learning resources, the dataset is then tested. Sensitivity, accuracy, and specificity are the performance metrics used for the proposed CKD classification approach. The results of the experiments demonstrate that the proposed approach outperforms the present state-of-the-art method in classifying CKD.