随机森林算法集成分类器处理不平衡医疗数据

M. Anbarasi, V. Janani
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引用次数: 5

摘要

在今天的生活中,随着医疗环境的快速增长,数据产生了大量的数据。医疗行业有大量的数据集,用于诊断和维护患者的记录。医学研究每天都有新的治疗方法和药物。但医疗数据集的可用性往往不平衡在他们的类标签。现有的一些方法在不平衡数据集上的性能较差。因此,从不平衡的数据中预测疾病变得难以处理。在这个建议中,分类器集成方法(随机森林算法)可以用来克服现有的分类器技术。多分类器系统比现有的分类器技术具有更高的准确性和鲁棒性。事实证明,集成方法在从现有的不平衡医疗保健患者数据中对记录进行分类方面非常有效,因为它涉及到考虑来自多个基本分类器的意见的过程,而不是单一分类器方法。该方法通过多个基分类器去除不相关的数据,给出了非常准确和精确的推断。医疗数据集的问题,特别是具有一些不确定性的问题是可以预测的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble classifier with Random Forest algorithm to deal with imbalanced healthcare data
In day today life, data is generated in massive amount with rapid growth in health care environment. The medical industries have large amount of data sets, for diagnosis purpose and maintain patient's records. The medical researches come with new treatments and medicine every day. But availability of medical datasets is often not balanced in their class labels. The performance of some existing method is poor on imbalanced dataset. So the prediction of disease from imbalanced data becomes difficult to handle. In this proposal Classifier ensemble method (Random Forest algorithm) can be used to overcome existing classifier techniques. Multiple classifier system is more accurate and robust than an existing classifier technique. The ensemble method proves to be very efficient in classification of records from available imbalanced healthcare patient data, as it involves the process of considering opinion from multiple base classifiers, as opposed to the single classifier method. This method gives a very accurate and precise inference, as unrelated data's are removed because of multiple base classifiers. The problems of healthcare dataset especially with some uncertainty can be predicted.
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