在医学数据挖掘领域,利用图像处理技术提高自适应分类器的分类精度

Sneha Chandra, Maneet Kaur
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引用次数: 6

摘要

医疗数据挖掘是数据挖掘中最具挑战性的领域之一。最大的挑战在于如何以较高的分类准确率对疾病进行分类。在本研究工作中,图像处理技术已用于我们的自适应分类器的高级版本,以生成样本医学数据集属性的类别。我们的自适应分类器的高级版本是使用聚类数据挖掘与分类数据挖掘相结合的技术生成的。提出的方法适用于样本医疗数据集,并将我们的自适应分类器的结果与其组成分类器的结果进行比较。实验结果表明,我们的自适应分类器具有较高的分类精度,这引起了人们对进一步分析的好奇心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancement of classification accuracy of our Adaptive Classifier using image processing techniques in the field of Medical Data Mining
Medical Data Mining is one of the most challenging fields of Data Mining. The greatest challenge lies in classifying the diseases with high classification accuracy. In this research work, image processing techniques have been used on the advanced version of our Adaptive Classifier, to generate categories for the attributes of sample medical datasets. The advanced version of our Adaptive Classifier has been generated using the techniques of Clustering Data Mining in conjunction with Classification Data Mining. The proposed approach works upon the sample medical datasets, and compares the results of our Adaptive Classifier with the results of its constituent classifiers. The experimental results generated showed higher classification accuracy for our Adaptive Classifier, which has rightly aroused the curiosity required for further analysis.
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