Prognosis of Thyroid Disease Using MS-Apriori Improved Decision Tree

  • Yuwei Hao
  • Wanli Zuo
  • Zhenkun ShiEmail author
  • Lin Yue
  • Shuai Xue
  • Fengling He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)


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.


MS-Apriori Decision tree Medical mining Disease predication 



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).


  1. 1.
    Fan, M., Hu, J., Cao, R., et al.: A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence. Chemosphere 200, 330–343 (2018)CrossRefGoogle Scholar
  2. 2.
    Jiang, F., Jiang, Y., Zhi, H., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)CrossRefGoogle Scholar
  3. 3.
    Jiang, H., Zhang, Z., Tao, L.: A semantic-based EMRs integration framework for diagnosis decision-making. In: Buchmann, R., Kifor, C.V., Yu, J. (eds.) KSEM 2014. LNCS (LNAI), vol. 8793, pp. 380–387. Springer, Cham (2014). Scholar
  4. 4.
    Fang, R., Pouyanfar, S., Yang, Y., et al.: Computational health informatics in the big data age: a survey. ACM Comput. Surv. 49(1), 12 (2016)CrossRefGoogle Scholar
  5. 5.
    Vemulapalli, V., Qu, J., Garren, J.M., et al.: Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data. Artif. Intell. Med. 74, 1–8 (2016)CrossRefGoogle Scholar
  6. 6.
    Tomaszewski, J.J., Uzzo, R.G., Egleston, B., et al.: Coupling of prostate and thyroid cancer diagnoses in the United States. Ann. Surg. Oncol. 22(3), 1043–1049 (2015)CrossRefGoogle Scholar
  7. 7.
    Akın, Ş., Yazgan, A.D., Akın, S., et al.: Prediction of central lymph node metastasis in patientswith thyroid papillary microcarcinoma. Turk. J. Med. Sci. 47(6), 1723 (2017)CrossRefGoogle Scholar
  8. 8.
    Chen, H.L., Yang, B., Wang, G., et al.: A three-stage expert system based on support vector machines for thyroid disease diagnosis. J. Med. Syst. 36(3), 1953–1963 (2012)CrossRefGoogle Scholar
  9. 9.
    Makas, H., Yumusak, N.: A comprehensive study on thyroid diagnosis by neural networks and swarm intelligence. In: International Conference on Electronics, Computer and Computation, pp. 180–183. IEEE, Ankara (2014)Google Scholar
  10. 10.
    Pourahmad, S., Azad, M., Paydar, S.: Diagnosis of malignancy in thyroid tumors by multi-layer perceptron neural networks with different batch learning algorithms. Glob. J. Health Sci. 7(6), 46–54 (2015)CrossRefGoogle Scholar
  11. 11.
    Kaya, Y.A.: Fast intelligent diagnosis system for thyroid diseases based on extreme learning machine. Arch. Otolaryngol. Head Neck Surg. 15(1), 41–49 (2014)Google Scholar
  12. 12.
    Maysanjaya, I.M.D., Nugroho, H.A., Setiawan, N.A.: A comparison of classification methods on diagnosis of thyroid diseases. In: International Seminar on Intelligent Technology and ITS Applications, pp. 89–92. IEEE, Surabaya (2015)Google Scholar
  13. 13.
    Chaudhary, R., Sharma, S., Sharma, V.K.: Improving the performance of MS-Apriori algorithm using dynamic matrix technique and map-reduce framework. Int. J. Innov. Res. Sci. Technol. 2(5), 2349–6010 (2015)Google Scholar
  14. 14.
    Du, X., Sun, S., Hu, C., et al.: DeepPPI: boosting prediction of protein-protein interactions with deep neural networks. J. Chem. Inf. Model. 57(6), 1499–1510 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbol Computation and Knowledge EngineeringJilin University, Ministry of EducationChangchunChina
  3. 3.School of Computer Science and Information TechnologyNortheast Normal UniversityChangchunChina
  4. 4.The First Hospital of Jilin UniversityChangchunChina

Personalised recommendations