肿瘤学中的机器学习方法

Maya P. Todorova, Ginka Marinova
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引用次数: 0

摘要

恶性疾病是导致死亡的主要原因之一。在保加利亚,恶性疾病的登记是强制性的。国家癌症登记处提供的关于已登记患者、其心理社会困扰和医疗机构的信息为利用机器学习方法对大量数据进行研究提供了机会。该研究旨在应用CHART、Boosting和Bagging三种机器学习算法来创建分类模型,以确定医疗机构的评级和患者的痛苦程度。我们提出了在不同分类方法的比较研究中获得的结果。
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Methods of Machine Learning in Oncology
Malignant diseases are among the leading causes of mortality. The registration of malignant diseases in Bulgaria is mandatory. The information from the National Cancer Registry about the registered patients, their psychosocial distress and the medical establishments provides an opportunity to conduct research with machine learning methods on a large volume of data. The study aims to apply three machine learning algorithms - CHART, Boosting and Bagging to create classification models for determining the rating of a medical institution and the level of distress of a patient. We present results obtained in a comparative study of different classification methods.
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