AutoCaps-LSTM-GBNet: Autoencoder based Capsule Network with ensemble gradient boosting network for CKD classification

Abstract

Chronic Kidney Disease (CKD) is a condition which is life-threatening, progressive and if left untreated until an advanced stage of the disease, can cause a lot of problems and cost. Noisy clinical data, feature redundancy, inadequate representation learning, and poor generalization are common limitations of existing machine learning and Deep Learning models. In order to overcome these drawbacks, a hybrid deep ensemble model, namely AutoCaps-LSTM-GBNet, is proposed to predict the CKD, which comprises an Autoencoder (AE), Bidirectional Long Short-Term Memory (BiLSTM), Capsule Network (CapsNet), and Gradient Boosted Deep Neural Network (GB-DNN). The UCI data for CKD is initially pre-processed using KNN imputation, Z-score normalization, SMOTE, and Recursive Feature Elimination (RFE). The Autoencoder captures compact latent features and BiLSTM and CapsNet capture complementary temporal and hierarchical representations which are fused and classified using GB-DNN. An experimental evaluation with repeated stratified 5-fold cross validation is performed, which achieves an accuracy of 96.82 ± 1.94%, AUC of 0.983 ± 0.011, precision of 0.972, recall of 0.965, specificity of 0.968 and F1-score of 0.968. The results show that this framework is well suited to making accurate, reliable, and robust early CKD prediction which could be a good candidate for computer-aided clinical decision-making support system