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首页> 外文会议>IEEE International Conference on Data Science and Advanced Analytics >An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates.
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An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates.

机译：用于预测肿瘤患者存活率的机器学习和防学习方法的集合。

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This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient's biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not.
机译：本文主要针对当时收集的患者的细胞，化学和物理条件有关的数据集进行地址，该数据集在其运作后进行术后去除结肠直肠癌。该数据在肿瘤去除点以及有关肿瘤分类和术后存活的信息时，对患者的生化和免疫状态提供了独特的洞察。基于TNM分期和生存率的肿瘤严重程度之间的关系仍不清楚TNM第2阶段和3颗肿瘤的患者。我们询问是否可以使用应用于数据子集的机器学习技术更准确地预测生存率，以获得对患者生物化学标记和生存之间的关系的更深入了解。我们使用一系列特征选择和单一分类技术来预测TNM第2阶段2和3例患者的5年生存率，其最初产生少于理想结果。然后将每个模型的性能与多个模型达到协议的数据的子集进行比较。这种选择性集合的新方法表明，对于在达到模型之间的一致达的患者，可以实现看不见的试验组上的模型精度的显着改进。最后，我们指出了可能的方法来识别是否可以准确预测患者的预后。

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