Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia

Abstract

Introduction The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than dialysis, graft failure is possible after transplantation. Hence, this study aimed to predict the risk of graft failure among post-transplant recipients in Ethiopia using the selected machine learning prediction models. Methodology The data was extracted from the retrospective cohort of kidney transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. In response to the imbalanced nature of the data, we performed hyperparameter tuning, probability threshold moving, tree-based ensemble learning, stacking ensemble learning, and probability calibrations to improve the prediction results. Merit-based selected probabilistic (logistic regression, naive Bayes, and artificial neural network) and tree-based ensemble (random forest, bagged tree, and stochastic gradient boosting) models were applied. Model comparison was performed in terms of discrimination and calibration performance. The best-performing model was then used to predict the risk of graft failure. Results A total of 278 completed cases were analyzed, with 21 graft failures and 3 events per predictor. Of these, 74.8% are male, and 25.2% are female, with a median age of 37. From the comparison of models at the individual level, the bagged tree and random forest have top and equal discrimination performance (AUC-ROC = 0.84). In contrast, the random forest has the best calibration performance (brier score = 0.045). Under testing the individual model as a meta-learner for stacking ensemble learning, the result of stochastic gradient boosting as a meta-learner has the top discrimination (AUC-ROC = 0.88) and calibration (brier score = 0.048) performance. Regarding feature importance, chronic rejection, blood urea nitrogen, number of post-transplant admissions, phosphorus level, acute rejection, and urological complications are the top predictors of graft failure. Conclusions Bagging, boosting, and stacking, with probability calibration, are good choices for clinical risk predictions working on imbalanced data. The data-driven probability threshold is more beneficial than the natural threshold of 0.5 to improve the prediction result from imbalanced data. Integrating various techniques in a systematic framework is a smart strategy to improve prediction results from imbalanced data. It is recommended for clinical experts in kidney transplantation to use the final calibrated model as a decision support system to predict the risk of graft failure for individual patients.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case–control study

“…However, in case–control studies with equal sizes, data balancing may not be necessary for ML algorithms [ 32 ]. When using ML algorithms, data balancing is generally important when there is an imbalance between classes, i.e., when one class has significantly fewer observations than the other [ 33 ]. In such cases, balancing can improve the performance of the algorithm by reducing the bias in favor of the majority class [ 34 ].…”

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

“…Eight machine learning models are chosen in this study, namely, logistic regression (LR), Naïve Bayesian, K-nearest neighbor (KNN), gradient boosted decision tree (GBDT), support vector machine (SVM), random forest (RF), extended gradient boost (XG Boost), and Adaptive boosting (Ada Boost) classifiers. Choice of this set of classifiers is to have a combination of classical models and advanced algorithms to meet the primary requirements of better accuracies and the suitability of classifiers for a limited and an imbalanced dataset [29]. All the eight classifiers are used in 'k' fold cross validation approach, where in the whole data set is split in to 'k' folds and the training and testing are repeated and the overall performance of the classifier is obtained as the average of each fold.…”

“…Scikit-learn library of Python open-source software 3.9.10 (Python software Foundation, Delaware, USA) [30] is used for the classification problem. Table 1 lists hyper parameters of some of these classifiers used in this work chosen based on the suggestions in literature to get the best possible classification [29]. Standard evaluation measures of classifiers viz., Sensitivity (Se), Specificity (Sp), Positive predictive value (PPV), Negative predictive value (NPV), Accuracy (Acc) and F1-Score (F1) are computed for each classifier and are compared.…”

A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data

“…Third, the simple threshold moving method can be applied. Mulugeta et al ( 32 ) used several ML algorithms such as LR, Naïve Bayes, ANN, RF, etc., with the threshold moving technique to predict the risk of graft failure on imbalanced kidney transplant recipients data. The results showed that the data-driven threshold moving technique improved the prediction result from imbalanced data compared to the natural threshold of 0.5.…”

“…In the algorithm-level technique, the cost or weight schema is used to mitigate the bias towards the majority class in the underlying classifiers or its output, which is famous as cost-sensitive learning ( 31 ). Compared with data-level techniques, this technique does not require the alteration of the original data distribution as the modified algorithms consider the uneven distribution of classes while training, which results in more accurate performance than data sampling techniques ( 32 ). In addition, a simple and straightforward method named threshold-moving has also shown effective results for the class imbalance problem, which moves the decision threshold in the output to make the high-cost samples harder to misclassify ( 33 , 34 ).…”