###### Abstract  
Chronic kidney disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease. To alleviate such issue, investment in early prediction is necessary. The purpose of this study is to assist the early prediction of CKD, addressing problems related to imbalanced and limited-size datasets. We used data from medical records of Brazilians with or without a diagnosis of CKD, containing the following attributes: hypertension, diabetes mellitus, creatinine, urea, albuminuria, age, gender, and glomerular filtration rate. We present an oversampling approach based on manual and automated augmentation. We experimented with the synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, and Borderline-SMOTE SVM. We implemented models based on the algorithms: decision tree (DT), random forest, and multi-class AdaBoosted DTs. We also applied the overall local accuracy and local class accuracy methods for dynamic classifier selection; and the k-nearest oracles-union, k-nearest oracles-eliminate, and META-DES for dynamic ensemble selection. We analyzed the models' performances using the hold-out validation, multiple stratified cross-validation (CV), and nested CV. The DT model presented the highest accuracy score (98.99%) using the manual augmentation and SMOTE. Our approach can assist in designing systems for the early prediction of CKD using imbalanced and limited-size datasets. p 2022 202  
and monitoring focusing on a dataset from people who live in Brazil, a continental-size developing country. Developing countries stand for low- and middle-income regions, while developed countries are high-income regions, such as the USA [5]. Developing countries suffer from increased mortality rates caused by chronic diseases, e.g., CKD, arterial hypertension (AH), and DM [6]. AH and DM are two of the most common CKD risk factors. People with type 1 or type 2 DM are at high risk of developing diabetic nephropathy [7], while severe AH cases may increase kidney damage. For example, in 2019, about 10 percent of the adult Brazilian population was aware of having kidney damage, while about 70 percent remained undiagnosed [8]. The CKD is characterized by permanent damage, reducing the kidneys' excretory function, measured using glomerular filtration [9]. However, the diagnosis usually occurs during more advanced stages because it is asymptomatic, postponing the application of countermeasures, decreasing people's quality of life, and possibly leading to lethal kidney damage. For example, in 2010, about 500-650 people per million of the Brazilian population faced dialysis and kidney transplantation [10]. This number has grown, warning governments about the relevance of the CKD early prediction. In 2016, according to the Brazilian chronic dialysis survey, the number of patients under dialysis was 122,825.00, increasing this number by 31,000.00 in the last five years [11]. In 2017, the prevalence and incidence rates of patients under dialysis were 610 and 194 per million [12]. The incidence continued to be high in 2018 (133,464.00) [13]. Estimates also indicate that, in 2030, about 4 million patients will be under dialysis worldwide [14]. The high prevalence and incidence of dialysis and kidney transplantation increase public health costs. Therefore, CKD has an expressive impact on the health economics perspective [15]. For instance, the Brazilian Ministry of Health reported that transplantation and its procedures spent about 720 million reais in 2008 and 1.3 billion in 2015 [16]. According to the Brazilian Ministry of Health, in 2020, the Brazilian government spent more than 1.4 billion reais for hemodialysis procedures. The costs and the high rates of persons waiting for transplantation suggest the increased public spending on kidney diseases. Preventing CKD has a relevant role in reducing mortality rates and public health costs [17].