AI Driven Prognosis in Pediatric Bone Marrow Transplantation Survival

EasyChair Preprint 15998

Abstract

Pediatric Bone Marrow Transplantation (BMT) is widely used as a treatment innovation that can treat certain types of cancers and hematologic disorders in children. Nevertheless, it is quite worrying that despite all the scientific breakthroughs in medical field, the survival rates for post BMT pediatric patients are still very low. Our study aims to do so by applying AI and ML approaches which will, in turn, improve the probabilistic forecast of pediatric BMT survival rates. Having a large dataset of demographic and clinical characteristics of pediatric patients, we subjected the data to an elaborate data cleaning process. This included handling of missing records, converting categorical variables into dummy and dealing with an uneven distribution of the survival status using the Borderline SMOTE method. Then we used mutual information for selecting the features, which helped in the elimination of the non-relevant characteristics. The selected features along with the full features dataset were fed to the following ML models namely Random Forest, XGBoost, Logistic Regression and Decision Tree through Hyperparameter optimization. The results of our study showed that the XGBoost model was the most efficient in identifying the survival status of the pediatric patients after BMT with an accuracy of 97.37% and precision of 100%. By facilitating more accurate predictions of survival outcomes, we can equip healthcare professionals with the insights necessary to make more informed clinical decisions, thereby potentially enhancing survival rates for children undergoing BMT.

Keyphrases: Acute Graft Versus Host Disease, Acute Lymphoblastic Leukemia, Bone marrow transplant, Borderline-SMOTE, Grid Search Cross-Validation, Hyperparameter Optimization, Pediatric BMT, Random Forest Algorithm, XGBoost, borderline smote algorithm, categorical variables, children undergoing hematopoietic stem cell, feature selection based, full feature set, machine learning, minority class, mutual information, quality of life, survival prediction of children undergoing hematopoietic, undergoing hematopoietic stem cell transplantation

@booklet{EasyChair:15998,
  author    = {Wafiya Sohail and Farhan Shoukat and Yasir Ali},
  title     = {AI Driven Prognosis in Pediatric Bone Marrow Transplantation Survival},
  howpublished = {EasyChair Preprint 15998},
  year      = {EasyChair, 2025}}