Survival Prediction of Children after Bone Marrow Transplant Using Machine Learning Algorithms

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Bone marrow is the source of many blood-related diseases, such as blood cancers, and Bone Marrow Transplantation (BMT), also known as Hematopoietic Stem Cell Transplantation (HSCT), is a life-saving surgical procedure. However, this treatment is associated with a high risk of mortality. Predicting survival after BMT is therefore essential for effective and accurate treatment. BMT is considered a treatment-related mortality due to several primary causes of death such as infections, toxicity, and Graft-versus-Host Disease (GvHD) that occur after treatment. In addition, several risk factors affect the success of BMT and long-term survival after treatment. Therefore, there is a need for a prediction system based on machine learning techniques that can predict whether the patient will survive after BMT or not, which will definitely help the physicians to make the right decisions before performing the surgery for the patient. In this paper, using a publicly available BMT dataset from the University of California, Irvine ML repository (UCI ML repository), different machine learning models were investigated to predict the survival status of children undergoing BMT treatment. In particular, Random Forest (RF), Bagging Classifier, Extreme Gradient Boost (XGBoost), Adaptive Boosting (AdaBoost), Decision Tree (DT), Gradient Boost (GB), and K-Nearest Neighbors (KNN) were trained on the given dataset. The dataset consists of 45 variables after applying a series of preprocessing steps and removing the multicollinearity features based on the correlation heat map. Then, a feature engineering and modelling step was applied to identify the most significant features, followed by the use of machine learning models to simplify the overall classification process. It’s important to note that the most important features obtained by DT and those obtained by GB were the most suitable for training the Bagging classifier and the KNN model, respectively. In addition to that, hyper-parameters optimization using Grid Search Cross-Validation (GSCV) was applied to both approaches to improve the accuracy of the survival prediction. RF, AdaBoost, GB, and Bagging techniques have achieved the best accuracy of 97.37%