## 1 Introduction  
Kidney diseases are rising daily and millions of people worldwide are affected due to these diseases. Sometimes, this causes a fatal medical ailment leading to place strain on both patients and the healthcare system [1]. Timely and correct diagnosis of kidney diseases is only possible due to effective management and treatment [2]. Electrocardiogram (ECG) data is valuable not only for treating and predicting cardiovascular health but also holds promise for the early identification and prediction of renal diseases [3]. Traditionally used to diagnose heart-related issues, ECG readings contain complex patterns that can reflect various physiological states. The close relationship between kidney function and circulatory dynamics is well-known, suggesting that variations in ECG signals might indicate changes related to kidney health [4]. Machine learning (ML) has recently transformed medical science and diagnostics, demonstrating to be extremely successful in tasks such as categorization, pattern recognition, and prediction [5]. This has prompted research into using various machine-learningalgorithms for kidney disease prediction using ECG data [6]. However, creative models are still required to fully realize the promise of ECG data and improve forecast accuracy. ECGs are valued for their affordability, accessibility, and quick results in diagnosing medical conditions. Deep learning algorithms (DLA) are now being applied to ECG analysis, offering the potential to predict outcomes, identify subclinical disorders, and reveal systemic phenotypes. CKD patients often exhibit cardiovascular risk factors and subclinical cardiac changes, including myocardial fibrosis, which may appear early in the disease. Electrolyte disorders common in CKD, such as hypokalemia and hyperkalemia, manifest as distinct ECG patterns, aiding in their detection. However, these abnormalities are subtle and may not be easily discernible by humans, making DLA-assisted screening valuable for identifying asymptomatic CKD patients who require further evaluation [7]. The need for prompt and non-invasive diagnostic tools underscores the value of detecting abnormal ECG patterns potentially. Traditional CKD diagnosis primarily relies on urine and blood tests, such as serum creatinine and glomerular filtration rate, which can be time-consuming, invasive, and may not immediately reflect disease progression. By leveraging ECG data, which is readily accessible, this approach offers the potential for early identification of cardiac issues related to CKD, facilitating timely treatment and ongoing monitoring of individuals at risk. This work aims to advance the detection of abnormal ECG patterns by utilizing the LADTree model. This model incorporates logistic boosting to construct an alternating decision tree, where each iteration selects a single attribute test as the splitter node. The model retains per-class weights and a functional response for each training instance. By minimizing the least-squares error between examples, the functional response is adjusted to fit the mean value of instances within a specific subset, enhancing the model's predictive capabilities for detecting abnormal ECG patterns. The LADTree is compared against several well-established ML models, including K-nearest Neighbor (KNN), Multilayer Perceptron (MLP), Naive Bayes (NB), Support Vector Machine (SVM), and J48-Decision Tree (J48), are used to assess LADTree's performance. Accuracy and error rate analysis are the two assessment measures used to evaluate these models. While accuracy is assessed based on Recall, Precision-Recall Curve (PRC) Area, Receiver Operating Characteristic Curve (ROC) Area, and Classification Accuracy (CA), error rates are calculated using Kappa Statistics (KS) and Mean Absolute Error (MAE). Unlike other models, LADTree's unique hierarchical structure and interpretability offer significant advantages, making it particularly effective in capturing complex relationships within ECG data for CKD prediction.