  Bone Marrow Transplant, a gradational rescue for a wide range of disorders
emanating from the bone marrow, is an efficacious surgical treatment. Several
risk factors, such as post-transplant illnesses, new malignancies, and even
organ damage, can impair long-term survival. Therefore, technologies like
Machine Learning are deployed for investigating the survival prediction of BMT
receivers along with the influences that limit their resilience. In this study,
an efficient survival classification model is presented in a comprehensive
manner, incorporating the Chi-squared feature selection method to address the
dimensionality problem and Hyper Parameter Optimization (HPO) to increase
accuracy. A synthetic dataset is generated by imputing the missing values,
transforming the data using dummy variable encoding, and compressing the
dataset from 59 features to the 11 most correlated features using Chi-squared
feature selection. The dataset was split into train and test sets at a ratio of
80:20, and the hyperparameters were optimized using Grid Search
Cross-Validation. Several supervised ML methods were trained in this regard,
like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors,
Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have
been performed for both the default and optimized hyperparameters by using the
original and reduced synthetic dataset. After ranking the features using the
Chi-squared test, it was observed that the top 11 features with HPO, resulted
in the same accuracy of prediction (94.73%) as the entire dataset with default
parameters. Moreover, this approach requires less time and resources for
predicting the survivability of children undergoing BMT. Hence, the proposed
approach may aid in the development of a computer-aided diagnostic system with
satisfactory accuracy and minimal computation time by utilizing medical data
records.
