Exploring Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms for Small and Imbalanced Datasets

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.

A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease

“…Hence, the proposed approach is compared with the following methods: a probabilistic neural network (PNN) [ 66 ], an enhanced sparse autoencoder (SAE) neural network [ 10 ], a naïve Bayes (NB) classifier with feature selection [ 67 ], a feature selection method based on cost-sensitive ensemble and random forest [ 3 ], a linear support vector machine (LSVM) and synthetic minority oversampling technique (SMOTE) [ 11 ], a cost-sensitive random forest [ 68 ], a feature selection method based on recursive feature elimination (RFE) and artificial neural network (ANN) [ 69 ], a correlation-based feature selection (CFS) and ANN [ 69 ]. The other methods include optimal subset regression (OSR) and random forest [ 9 ], an approach to identify the essential CKD features using improved linear discriminant analysis (LDA) [ 13 ], a deep belief network (DBN) with Softmax classifier [ 70 ], a random forest (RF) classifier with feature selection (FS) [ 71 ], a model based on decision tree and the SMOTE technique [ 12 ], a logistic regression (LR) classifier with recursive feature elimination (RFE) technique [ 14 ], and an XGBoost model with a feature selection approach combining the extra tree classifier (ETC), univariate selection (US), and RFE [ 15 ].…”

“…Furthermore, Silveira et al [ 12 ] developed a CKD prediction approach using a variety of resampling techniques and ML algorithms. The resampling techniques include the synthetic minority oversampling technique (SMOTE) and Borderline-SMOTE, while the classifiers include random forest, decision tree, and AdaBoost.…”

Siamese Convolutional Neural Network for Heartbeat Classification Using Limited 12-Lead ECG Datasets

“…Considered an open problem in the context of deep learning with ECG signals [4], the academy and industry recognize the class imbalance as an obstacle in developing effective deep learning models with a high amount of parameters by making the training phase harder. ML practitioners can avoid this problem using data augmentation techniques such as the Synthetic Minority Oversampling Technique (SMOTE) [5]. Recently, a new approach called few-shot learning [6] has been popularized and stands out in imaging processing problems.…”

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques

“…This study aims to construct an effective hybrid important risk factor evaluation scheme for CKD stages 3a and 3b patients with MetS, based on ML predictive models. Our study used six well-known and effective ML techniques—random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and light gradient boosting machine (LightGBM)—to develop ML predictive models [ 16 , 18 , 19 , 37 , 38 , 39 ]. The important risk factors identification results can provide valuable information regarding the prevention of CKD and health promotion.…”