Neuroblastoma is a pediatric cancer with high morbidity and mortality. Accurate survival prediction of patients with neuroblastoma plays an important role in the formulation of treatment plans. In this study, we proposed a heterogeneous ensemble learning method to predict the survival of neuroblastoma patients and extract decision rules from the proposed method to assist doctors in making decisions. After data preprocessing, five heterogeneous base learners were developed, which consisted of decision tree, random forest, support vector machine based on genetic algorithm, extreme gradient boosting and light gradient boosting machine. Subsequently, a heterogeneous feature selection method was devised to obtain the optimal feature subset of each base learner, and the optimal feature subset of each base learner guided the construction of the base learners as a priori knowledge. Furthermore, an area under curve-based ensemble mechanism was proposed to integrate the five heterogeneous base learners. Finally, the proposed method was compared with mainstream machine learning methods from different indicators, and valuable information was extracted by using the partial dependency plot analysis method and rule-extracted method from the proposed method. Experimental results show that the proposed method achieves an accuracy of 91.64%, recall of 91.14%, and AUC of 91.35% and is significantly better than the mainstream machine learning methods. In addition, interpretable rules with accuracy higher than 0.900 and predicted responses are extracted from the proposed method. Our study can effectively improve the performance of the clinical decision support system to improve the survival of neuroblastoma patients.

中文翻译：

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一种用于神经母细胞瘤生存预测的异类集成学习方法。

神经母细胞瘤是一种具有高发病率和死亡率的小儿癌症。神经母细胞瘤患者的准确存活预测在制定治疗计划中起着重要作用。在这项研究中，我们提出了一种异类集成学习方法来预测神经母细胞瘤患者的生存，并从该方法中提取决策规则，以帮助医生做出决策。经过数据预处理，开发了五种异构基础学习器，包括决策树，随机森林，基于遗传算法的支持向量机，极限梯度提升和光梯度提升机。随后，设计了一种异构特征选择方法以获得每个基础学习者的最佳特征子集，每个基础学习者的最佳特征子集指导了基础学习者的构建，将其作为先验知识。此外，提出了一种基于曲线的集成机制下的区域，以整合五个异类基础学习者。最后，将本文提出的方法与主流机器学习方法从不同指标上进行了比较，并通过偏依赖图分析法和规则提取法从有价值的信息中提取了有价值的信息。实验结果表明，该方法的准确率达到91.64％，召回率达到91.14％，AUC达到91.35％，明显优于主流机器学习方法。此外，从提出的方法中提取了可解释的规则，其准确性高于0.900，并预测了响应。