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首页> 外文会议>International Conference on Advances in ICT for Emerging Regions >Machine Learning Approach to Predict the Survival Time of Childhood Acute Lymphoblastic Leukemia Patients
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Machine Learning Approach to Predict the Survival Time of Childhood Acute Lymphoblastic Leukemia Patients

机译：机器学习方法预测儿童急性淋巴细胞白血病患者的生存时间

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摘要

Survival time prediction (prognosis) is the task of predicting the length of time that a patient will survive. Survival time prediction is a difficult task due to the complex relationships that exist among biological, genetic and environmental factors. Medical practitioners make predictions about the survival time using their previous experiences and observations. The prognosis for different practitioners is often inconsistent. An accurate survival time prediction model can help in treatment scheduling, care of cancer patients and increase the quality of healthcare. In this paper, we present the results of an exploratory study of the survival time prediction of Childhood Acute Lymphoblastic Leukemia patients using a dataset of 512 patients provided by Maharagama National Cancer Institute, Sri Lanka. We investigated three machine learning techniques including multiple linear regression, regression trees and support vector regression. The performances of the models were evaluated using the Relative Absolute Error and Concordance Index in combination with 5-fold cross-validation. Our experiments show that the multiple linear regression and the support vector regression are effective: each predictor achieved an average cross-validated RAE less than 0.3, which is significantly lower than values reported in the previous studies. We also use our prediction models to classify each patient into short survivor versus long survivor where the classification boundary is the average survival time of the entire population. All the prediction modes achieved more than 70% classification accuracy.
机译：生存时间预测（预后）是预测患者生存时间长度的任务。由于生物学，遗传和环境因素之间存在复杂的关系，因此生存时间的预测是一项艰巨的任务。医生根据他们以前的经验和观察结果对生存时间做出预测。不同从业者的预后往往不一致。准确的生存时间预测模型可以帮助制定治疗计划，照顾癌症患者并提高医疗质量。在本文中，我们使用斯里兰卡Maharagama国家癌症研究所提供的512名患者的数据集，对儿童急性淋巴细胞白血病患者的生存时间预测进行了探索性研究，并提出了研究结果。我们研究了三种机器学习技术，包括多元线性回归，回归树和支持向量回归。使用相对绝对误差和一致性指数结合5倍交叉验证来评估模型的性能。我们的实验表明多元线性回归和支持向量回归是有效的：每个预测变量的平均交叉验证RAE均小于0.3，这明显低于先前研究中报告的值。我们还使用预测模型将每个患者分为短期幸存者和长期幸存者，其中分类边界是整个人群的平均生存时间。所有的预测模式都达到了70％以上的分类精度。

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