Abstract
The lymph nodes metastasis in the papillary thyroid microcarcinoma (PTMC) can lead to a recurrence of cancer. We hope to take preventive measures to reduce the recurrence rate of the thyroid cancer. This paper presents a decision tree improved by MS-Apriori for the prognosis of lymph node metastasis (LNM) in patients with PTMC, called MsaDtd (Decision tree Diagnosis based on MS-Apriori). The method converts the original feature space into a more abundant feature space, MS-Apriori is used to generate association rules that consider rare items by multiple supports and fuzzy logic is introduced to map attribute values to different subintervals. Then, we filter the ranked rules which consider positive and negative tuples. We improve accuracy through deleting disturbance rules. At last, we use the decision tree to predict LNM by analyzing the affiliation between the instance and rules. Clinical-pathological data were obtained from the First Hospital of Jilin University. The results show that the proposed MsaDtd achieves better prediction performance than other methods on the prognosis of LNM.
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Acknowledgement
Project supported by the Nature Science Foundation of Jilin Province (No. 20180101330JC), the National Nature Science Foundation of China (No. 60973040), the Fundamental Research Funds for the Central Universities (No. 2412017QD028), China Postdoctoral Science Foundation (No. 2017M621192), the Scientific and Technological Development Program of Jilin Province (No. 20180520022JH).
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Hao, Y., Zuo, W., Shi, Z., Yue, L., Xue, S., He, F. (2018). Prognosis of Thyroid Disease Using MS-Apriori Improved Decision Tree. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_40
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