A Classification Method of Thyroid Disease Based on Rotation Forest

doi:10.3969/j.issn.1006-2475.2016.03.003

Abstract

Abstract: Thyroid disease is common in the field of endocrine, accurate identification of different types of thyroid disease is the primary problem of clinical treatment. By using the results of clinical experiments, this paper presents a new method for thyroid disease classification. The method uses principal component analysis to reduce data dimension, and then implements classification task based on rotation forest algorithm. Rotation forest algorithm can make the difference between the base classifiers more obvious, and then improve the accuracy of the classifier, and it can reduce the processing time at the same time. Experimental results show that the classification accuracy of this method can reach to 96.28% on the dataset from UCI machine learning repository. In order to verify the effectiveness of the method furthermore, this paper also chooses the real clinical medical data set, it is more complex than the UCI standard dataset in data quantity and data dimension. Compared with the other method, the classification accuracy of this method reaches to 96.37%.

Key words: thyroid disease, ensemble classification, rotation forest, feature selection, principal component analysis

CLC Number:

TP391

PAN Qiao, XU Teng, CHEN De-hua, XU Guang-wei. A Classification Method of Thyroid Disease Based on Rotation Forest[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.03.003.

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