不同机器学习分类器对造血干细胞移植儿童生存预测的回顾性分析
Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis
摘要：
骨髓移植是一种有效的外科治疗方法，它是一种对骨髓源性疾病进行分级抢救的方法。一些风险因素，如移植后疾病，新的恶性肿瘤，甚至器官损伤，可能会损害长期生存。因此，像机器学习这样的技术被部署用于研究BMT接收器的生存预测以及限制其弹性的影响。在这项研究中，一个有效的生存分类模型，提出了一个全面的方式，结合卡方特征选择方法来解决维度问题和超参数优化（HPO），以提高准确性。合成数据集是通过估算缺失值，使用虚拟变量编码转换数据，并使用卡方特征选择将数据集从59个特征压缩为11个最相关的特征而生成的。将数据集以80：20的比例分成训练集和测试集，并使用网格搜索交叉验证优化超参数。在这方面，我们训练了几种有监督的ML方法，如决策树、随机森林、逻辑回归、K最近邻、梯度提升分类器、Ada Boost和XG Boost。通过使用原始和简化的合成数据集，对默认和优化的超参数进行了模拟。在使用卡方检验对特征进行排名后，观察到HPO的前11个特征导致与具有默认参数的整个数据集相同的预测准确度（94.73%）。此外，这种方法需要更少的时间和资源来预测接受骨髓移植的儿童的生存能力。因此，所提出的方法可以帮助开发一个计算机辅助诊断系统，具有令人满意的准确性和最小的计算时间，通过利用医疗数据记录。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
