Research Article Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis Ishrak Jahan Ratul , 1 Ummay Habiba Wani , 1 Mirza Muntasir Nishat , 1 Abdullah Al-Monsur , 1 Abrar Mohammad Ar-Rafi , 1 Fahim Faisal , 1 and Mohammad Ridwan Kabir 2 1 Department of EEE, Islamic University of Technology, Gazipur, Bangladesh 2 Department of CSE, Islamic University of Technology, Gazipur, Bangladesh Correspondence should be addressed to Mirza Muntasir Nishat; mirzamuntasir@iut-dhaka.edu Received 9 May 2022; Revised 31 August 2022; Accepted 5 September 2022; Published 25 September 2022 Academic Editor: Rajesh Kaluri Copyright © 2022 Ishrak Jahan Ratul et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. 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. 1. Introduction Cancer kills millions of people, even in its most curable forms. According to the statistics of 2020, the estimated death tolls in the USA from colon, pancreatic, lung, breast, and prostate cancers are 53200, 47050, 135720, 42690, and 33330, respectively [1]. When there is no cure, physicians endeavor to extend the lifespan of a cancer patient through surgery, radiation therapy, or chemotherapy as alternative methods of cancer treatment [2]. For various reasons, the high dose of medication during chemotherapy or radiation therapy causes bone marrow damage in patients [2]. Bone marrow (BM), a delicate, elastic adipose tissue located inside most skeleton structures, is responsible for creating the red blood cells of human blood [3, 4]. It also contains hemato- poietic stem cells (HSC) that are merely immature blood- forming stem cells endowed with idiosyncratic properties like self-renewal, and they form populations of progenitor cells through cell division and differentiation [4–6]. How- ever, the concept of BMT, otherwise known as hematopoie- tic stem cell transplant (HSCT), gleans from the postulation of eliminating dysfunctional body parts and replacing them Hindawi Computational and Mathematical Methods in Medicine Volume 2022, Article ID 9391136, 14 pages https://doi.org/10.1155/2022/9391136