# Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
## 1 Introduction
### Problem Statement  
An early diagnosis of such CKD is crucial and can save a patient's life. Medical experts utilized several fundamental approaches to gather precise insights about renal disease identification, such as medical examinations and lab tests (i.e., blood tests and urine tests). The blood test determines the glomerular filtration rate (GFR), which could be used to indicate kidney function. The urine test is used to determine albumin level, indicating whether the kidney is working correctly. With promising data sources that could help in medical diagnosis, developing robust and generalized diagnosis models that could assist medical experts and give accurate and timely decisions is crucial.  
Recently, machine learning (ML) has contributed to developing effective models in the medical diagnosis domain, which could make accurate and timely decisions. Deep learning (DL) is a subsection of ML that seeks to find underlying links within a dataset through a set of operations that occurred while training. DL is a multilayer DL model that could theoretically handle nonlinear data; it significantly affects medical applications. For example, [PERSON] et al. [9] utilized DL to develop a predictive model that predicts CKD based on several medical examination data. The same statement applies to [10]. Unless DL progresses in various applications, it has several limitations due to the heterogeneity of the medical data, which exacerbates the generalization and robustness of the developed model, resulting in misleading rules and reproducible diagnostic models. Therefore, the training process in DL does not always guarantee achievement of optimal weights and may end with a high-variance model. This challenge could be overcome by using diverse and various DL models. This process is called ensemble learning. Ensemble learning handles single model limitations by combining the advantage of traditional and ensemble learning to give more flexibility and a more generalization model [11]. Ensemble learning has two main types: the base learner and the diversity [12]. First, homogenous learning is achieved by using different data samples. Secondly, heterogeneous learning is achieved by using other models. The many combinations utilized to construct ensemble classifiers include bagging [13], boosting [14], and stacking [15]. In our study, the stacking ensemble model provides a generalized, robust, and flexible model. Several studies demonstrated that ensemble learning results in an accurate and effective model.  
Choosing the optimal feature list is the main point in building an efficient model. Feature selection has been intensively investigated in the ML domain, achieving promising results in biomedical applications. Feature selections are classified into three main types: wrapper, filter, and embedded [16]. Our study utilized four feature selection methods to choose the optimal feature list. According to all the above, the main objective of the current paper is to develop an ensemble DL that could improve the prediction performance with the optimal feature subset. From a clinical perspective, our proposed feature list demonstrates the effectiveness in early prediction of CKD compared with the state of the art.