## 2 Literature Study  
Advanced artificial intelligence (AI) and ML techniques have greatly improved modern healthcare systems by enabling more accurate patient analysis and customized treatments. Revathy et al. 's work [8] addressed the problems by analyzing and predicting the condition using a variety of ML models. They conducted a comparative study based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC), and they gave a thorough description of these models. Their research demonstrated the potential of predictive models to enhance healthcare outcomes and resulted in the creation of the most effective model for CKD prediction. In their conclusion, they emphasized the role that machine learning plays in early CKD prediction. E.P.B. Multia et al. discovered that renal illness may be anticipated through the use of classification algorithms to analyze patients' ECG data. According to recent research, people with kidney illness frequently encounter cardiac problems called cardiorenal syndrome (CRS), which can result in unexpected cardiac death [9]. ML models can be used by patients with cardiovascular issues to assess whether cardiovascular illness and CRS are affecting their kidneys [10]. Another research by Nusinovici et al. examined the effectiveness of several machine learning models in forecasting hypertension (HTN), diabetes (DM), cardiovascular disease (CVD), and CKD using fundamental clinical markers [11]. Using data from 6762 Asian people, they assessed five machine learning models: neural networks (NN), SVM, random forests (RF), gradient boosting machines, and k-nearest neighbor with logistic regression (LR). According to their findings, LR was more successful at predicting CKD and DM, whereas NN and SVM were more successful at predicting CKD and HTN. In the field of healthcare, clinical disorders must be recognized, prevented, and treated while simultaneously looking for economical and effective remedies. ML is an essential tool for accomplishing these objectives. Healthcare uses a variety of data types, including clinical and claims data. ML approaches analyze the links between diseases and the tests used to identify them by comparing various diagnostic procedures for each disease to their clinical relevance [12]. Patients with early-stage CKD might halt or even reverse the condition's course by taking the appropriate medical measures. The digitized ECG data for this model was obtained from the Physionet Database (www.physionet.org, accessed on 30 March 2024), which is combined with the publicly accessible Physikalisch-Technische Bundesanstalt (PTB) (for renal patients) and Fantasia (for healthy persons) databases. To assess the framework, more information was obtained from the same source. The method of testing produced exact findings, differentiating between people who had kidney infections and those who did not. Using both QT and RR interval values, the research outperformed the accuracy attained with a single feature, achieving a precision of 97.6% [13; 14]. In a different research, CKD patients were classified from a dataset using seven machine learning algorithms: NBTree, SVM, J48, MLP, LR, NB, and CHIRP. With an accuracy of 99.75%, CHIRP performed better than the other techniques, proving its usefulness in the early identification of CKD [15]. Density-based function selection (DFS) and ant colony-based optimization (D-ACO) were used by Elhoseny et al. [16] to provide an integrated CKD healthcare system. As used in conjunction with DFS to remove redundant features, the D-ACO method greatly increased classification accuracy on a benchmark CKD dataset as compared to earlier techniques. An effective method for the Kidney Disease Outcomes Quality Initiative (KDQQI) was developed by a team of internists and nephrologists to help primary care physicians diagnose and treat CKD, which is defined as having a glomerular filtration rate (GFR) of 60 mL/min/1.73 m\({}^{2}\) and/or signs of renal impairment for at least three months [17].