A machine learning framework to predict kidney graft failure with class imbalance using Red Deer algorithm

The prevalence of chronic kidney diseases has raised the demand for kidney replacement therapy over recent years. Although kidney transplantation provides better life quality than other alternatives, the replacement might face rejection and endanger individuals' lives. Therefore, predicting this rejection is of great importance, and due to the complexity of the human body's immune system, it is a complicated task. Machine learning has been widely used in disease diagnoses such as graft rejection. One of the common issues in disease diagnosis is the class imbalance problem. The predictive model is likely to misclassify all of the minority class samples in such cases. This paper proposes a method that can accurately predict kidney rejection in an imbalanced dataset. We have adopted kidney transplantation data from 378 patients collected from 1994 to 2011. Our main contribution is to develop a novel clustering method using Red Deer Algorithm (RDA), which is later utilized in proposing a three-stage clustering-based undersampling approach to handle the class imbalance problem so that the final predictive models can classify the given data more accurately. The undersampling method includes denoising, RDA clustering, and sample selection. Moreover, the proposed clustering method is also used to reduce the data dimensionality. Subsequently, five different classification algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Decision Tree (DT), and an ensemble method are used to predict kidney graft rejection. Then, these classifiers are compared in terms of five performance eval-uation metrics: accuracy, sensitivity, specificity, F1 score, and area under the Receiver Operating Characteristic (ROC) curve (AUC). The obtained results indicate that the decision tree model outperformed other algorithms and achieved 0.96, 0.94, 0.97, 0.95, and 0.95 for accuracy, sensitivity, specificity, F1 score, and AUC, respec-tively. Hence, it is highly recommended to use the proposed method as a decision support system for clinical experts to predict kidney transplantation failure.

Automated summary

The prevalence of chronic kidney diseases has increased the demand for kidney replacement therapy. This paper proposes a novel clustering method using Red Deer Algorithm (RDA) to accurately predict kidney rejection in an imbalanced dataset. The proposed method can serve as a decision support system for clinical experts to predict kidney transplantation failure.