###### Abstract  
It is a serious global health concern that chronic kidney disease (CKD) kills millions of people each year as a result of poor lifestyle choices and inherited factors. Effective prediction tools for prior detection are essential due to the growing number of patients with this disease. By utilizing machine learning (ML) approaches, this study aids specialists in studying precautionary measures for CKD through prior detection. The main objective of this paper is to predict and classify chronic kidney disease using ML approaches on a publicly available dataset. The dataset of CKD has been taken from the publicly available and accessible dataset Irvine ML Repository, which included 400 instances. ML methods (Support Vector Machine (SVM), K-Nearest Neighbors (KNN), random forest (RF), Logistic Regression (LR), and Decision Tree (DT) Classifier) are used as base learners and their performance has been compared with eXtreme Gradient Boosting (XGBoost). All ML algorithms are evaluated against different performance parameters: accuracy, recall, precision, and F1-measure. The results indicated that XGBoost outperformed with 98.00% accuracy as compared to other ML algorithms. For policymakers to forecast patterns of CKD in the population, the model put forth in this paper may be helpful. The model may enable careful monitoring of individuals who are at risk, early CKD detection, better resource allocation, and management that is patient-centered. chronic kidney disease (CKD); machine learning (ML); data preprocessing; ensemble methods; entropy +
Footnote †: journal: Medical  
utilized can interact with various organs and systems, resulting in a considerable reduction in life expectancy [3]. Chronic kidney disease is referred to as \"chronic\" because it develops gradually and often lasts for a long period, impairing the urinary system's function. High blood pressure, diabetes, as well as cardiovascular disease are all risk factors for CKD patients [4]. Patients with CKD experience adverse effects, particularly in the late stages that harm the neurological and immunological systems. Patients in countries that are developing may come to the end of their lives and require dialysis. GFR is calculated using information such as the patient's age, blood test results, gender, and other criteria [5]. The various parameters affecting CKD are shown in Figure 1.  
The patient's age, race, blood creatinine, gender, and other criteria can be used to assess chronic kidney disease. The severity of impairment caused by CKD is classified into five phases, as shown in Table 1, based on GFR [6]:  
As there is a growing number of CKD patients, there is a scarcity of CKD-specialized physicians [7]. Computer-assisted diagnostics are required to assist physicians as well as radiologists in making diagnostic judgments. Machine learning (ML) as well as deep learning (DL) methods have been used in the early phases of CKD prediction as well as diagnosis. Artificial intelligence (AI) techniques have played a major role in medical and health industries [8]. CKD is one of the key issues in medical science because it involves significant criteria and complexity to effectively forecast this disease. Algorithms, such as Naive Bayes (NB), Decision Trees (DTs), K-Nearest Neighbors (KNN), and Neural Networks, are used to predict CKD risk. Each algorithm has a specialty, such as Naive Bayes' use of probability to predict CKD, Decision Trees' use of classification to produce a classified report for CKD, and Neural Networks' use of opportunities to minimize CKD prediction error [9]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Description** & **GFR (mL/min)** \\ \hline I & Kidney function is normal & \(\geq\)90 \\ \hline II & Mild increase in GFR & 60–89 \\ \hline III & Moderate increase in GFR & 30–59 \\ \hline IV & Severe increase in GFR & 15–29 \\ \hline V & Kidney failure & 15 or dialysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different stages present in CKD. Figure 1: Factors affecting CKD.