## 2 Related Works  
Many academics are consistently working on the prediction of CKD using various classification algorithms. Numerous studies have been undertaken in the last few decades to better understand and investigate CKD. Many researchers have employed various classification strategies for the early detection of CKD in the literature. Data mining (DM) is the methodology of extracting specific data from a large dataset. Medical diagnosis, facial recognition, and data filtering are just a few of the uses of these techniques. The authors of one study [9] described an ultrasonography (USG)-based approach for diagnosing the phases of CKD. The algorithm works to detect fibrotic instances at various times. Authors of another study [10] performed a comparison of the outcomes of various models. They found and claimed that the Multiclass DT approach outperforms previous algorithms, with an accuracy of roughly 98% for a smaller dataset of 14 attributes. To decide whether the urinary system is harmless or harmful, the authors [11] suggested an expert system based on fuzzy logic. The authors [12] employed Softmax to categorize the final class after using a stacked autoencoder prototype to extract the features of CKD. In one study, the authors [13] introduced a genetic algorithm (GA) in which the GA optimized the weight vectors to train the NN. For CKD diagnosis, the system outperforms using standard neural networks. The authors of another study [14] used multiple machine learning classification algorithms to reduce diagnosis time and enhance the accuracy of diagnosis. The planned study looks at how different phases of CKD are classified based on their severity. The RBF algorithm performs better than other classifiers, with an accuracy of 85.3%. In one study, the authors [15] examined the missing values in the CKD dataset as they greatly decreased accuracy as well as prediction. Authors of one study [16] incorporated a unique methodology to figure out CKD using ML algorithms. The dataset consists of 400 instances with 24 dependent variables and one independent variable or class variable whose values are \"ckd\" or \"nockd.\" They used KNN, Neural Networks, and RF in order to obtain the results. [PERSON] [17] performed an investigation on the impact of class imbalance. Using a sampling algorithm, a comparison study was done. In another study, the authors [18] evaluated 12 alternative categorization algorithms. They calculated the accuracy of their prediction findings by comparing their calculated results to real results. Accuracy, sensitivity, precision, and specificity were utilized as assessment criteria. Clustering, classification, and other data mining techniques play an important role in extracting undisclosed knowledge from large databases. The technique of classification is one of the supervised learning techniques for defining subgroups ahead of time. The data attribute value must be used to identify the classes in the classification process. It establishes the classes by taking into account the quality of the data. These preconfigured specimens are used by the training algorithm to identify the set of parameters required for proper segregation [19; 20; 21]. In one article, the authors [22] utilized Apriori as well as k-means algorithms to construct a prediction system, and these were also employed to forecast individuals with kidney failure. The authors used machine learning algorithms to assess 42 data characteristics and used Receiver Operating Characteristic (ROC) plots to evaluate the data. The authors of one study [23] developed a diagnosis and prediction system and also used the tool Weka to evaluate the data. The authors analyzed ROC plots using the J48 classifier, AD Trees, the K-Star method, the NB classifier, and the random forest (RF) classifier. In their research, the K-Star algorithm and the RF classifier were found to be the best approaches for the dataset. [PERSON] [24] suggested a method for comparing the performance of two data mining approaches.