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A Novel Neighborhood Rough Set Based Classification Approach for Medical Diagnosis

机译：一种基于邻域粗糙集的医学诊断分类方法

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摘要

Medical datasets consume enormous amount of information about the patients, diseases and the physicians. Diseases diagnosis required many expensive tests to predict the diseases. Cost of disease prediction and diagnosis can be reduced by applying machine learning and data mining methods. Disease prediction and decision making plays asignificant role in medical diagnosis. In this study, a novel neighborhood rough set classification approach is presented to deal with medical datasets. Five benchmarked medical datasets have been used in this research work for studying the impact of proposed work in decision making.Experimental resultof the proposed classification algorithm is compared with other existing approaches such as rough set,Kth–nearest neighbor, support vector machine, Back propagation algorithm and multilayer perceptron to conclude that the proposed approach is cheaper way for disease prediction and decision making. The performance of classification algorithms measured based on various classification accuracy measures.
机译：医学数据集消耗了大量有关患者，疾病和医师的信息。疾病诊断需要许多昂贵的测试才能预测疾病。通过应用机器学习和数据挖掘方法，可以降低疾病预测和诊断的成本。疾病的预测和决策在医学诊断中起着重要的作用。在这项研究中，提出了一种新颖的邻域粗糙集分类方法来处理医学数据集。该研究工作使用了五个基准医学数据集来研究拟议工作对决策的影响。将拟议分类算法的实验结果与其他现有方法（例如粗糙集，Kth最近邻，支持向量机，反向传播）进行比较该算法和多层感知器得出结论，该方法是疾病预测和决策的较便宜方法。基于各种分类精度度量来度量的分类算法的性能。

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