Research of Data Mining on the Post-Treatment Survival Period Prediction of Colorectal Cancer

  • Xiufeng Liu
  • Zhenhu Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 216)


This paper highlighted the basic situation of colorectal cancer and introduced the key technologies of data mining, then summarized medical applications of data mining technologies, and finally discussed the use of data mining technology in cancer, especially colorectal cancer research. Prospect of data mining prediction in post-treatment of colorectal cancer has been proposed.


Data mining Survival period prediction Colorectal cancer 



This paper is supported by Guangdong Province Medical Research Foundation Project of Science and Technology, China (A2010209), and Guangdong Province Technology Project in Social Development Field of Guangdong Provincial Department of Science and Technology, China (201102).


  1. 1.
    Tonus C, Neupert G, Sellinger M (2006) Colorectal cancer screening by non-invasive metabolic biomarker fecal tumor M2-PK. World J Gastroenterol 12(43):7007–7011Google Scholar
  2. 2.
    GU GL, Wei XM, Wang SL (2007) Expression of, E-cadherin, β-catenin, matrix metalloproteinase-7 and its correlation to invasion/metastasis of hereditary nonpolyposis colorectal cancer. World Chin J Digestology 15(18):2032–2036Google Scholar
  3. 3.
    Dun MH (2002) Data mining: introductory and advanced topics, vol 24. Prentice Hall, New Jersey, pp 146–151Google Scholar
  4. 4.
    Buntine W (1993) Learning classification trees. In: Artificial intelligence frontiers in statistics, Chapman and Hall, London, pp 182–201Google Scholar
  5. 5.
    Anderberg MR (1973) Cluster analysis for applications, vol 02. Academic, New York, pp 24–27MATHGoogle Scholar
  6. 6.
    Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings ACM SIGMOD international conference management of data, vol 11. Washington, pp 207–216Google Scholar
  7. 7.
    Antonie ML, Zaiane OR, Coman AD (2001) Application of data mining techniques for medical image classification. In Proceedings of second international workshop on multimedia data mining(MDM/KDD’2001) in conjunction with seventh ACMSIGKDD, vol 07. San Francisco, vol 07, pp 94–101Google Scholar
  8. 8.
    Kusiak A, Kernstine KH, Kern JA (2010) Data mining: medical and engineering case studies. In: Proceedings of the industrial engineering research 2000 conference, vol 5(21). Cleveland, pp 1–7Google Scholar
  9. 9.
    Bojarczuk CC, Lopes HS, Freitas AA (1997) Data mining with constrained-syntax genetic programming: applications in medical data set. AA Freitas—Genet Program 42:255–257Google Scholar
  10. 10.
    Qiang Y, Guo Y, Zhang SJ (2008) Using data mining techniques to establish solitary pulmonary nodules diagnosis model. Chin J Med Imaging Technol 24(3):438–441Google Scholar
  11. 11.
    Chen JX, Xi GC, Wang W (2008) A comparison study of data mining algorithms in CHD clinical application. Beijing Biomed Eng 27(3):249–252Google Scholar
  12. 12.
    Peng G, Qu B, Miao H (2007) Association rules and its application in risk prediction of bacillary dysentery. Mod Prev Med 34(11):2007–2008Google Scholar
  13. 13.
    Ni Q, Jiang ZS, Gao QJ (2008) Prescription rules of type-2 diabetes mellitus with coronary heart disease by the structural case data collection system. Chin J Integr Med Cardio/Cerebrovasc Dis 1(6):7–8Google Scholar
  14. 14.
    Cao LH (2008) The pilot study of diagnosis of spleen deficiency syndrome by means of data mining. Sun Yat-sen Univ 35:24–27Google Scholar
  15. 15.
    Xiao-fen YU, Wang Z, Guo XC (2008) Data mining technique and its application in management against hospital infection in operating room. Chin J Nosocomiol 18(1):78–81Google Scholar
  16. 16.
    Cao MC, Tian F, He JF (2009) Hospital decision support system design based on data warehouse and data mining. Chin Digital Med 4(2):51–53Google Scholar
  17. 17.
    Wang XP, Li Y (2005) Application of data mining technology in electronic medical records system. Mod Prev Med 35(13):2450–2451Google Scholar
  18. 18.
    Chen XM, Xie XY, Li ZM (2005) Intelligent computer-aided diagnosis model research in PACS based on data mining. Comput Eng Des 26(5):1182–1184Google Scholar
  19. 19.
    Tan H, Pi MJ (2008) Construction of gallstone data inquiry and mining system, based on Web and SQL/ASP. J TCM Univ Hunan 28(3):72–74Google Scholar
  20. 20.
    Wu JH, He J, He X (2006) Association rules and their application in the analysis of the liver cancer patients data. Chin J Health Stat 23(1):34–38Google Scholar
  21. 21.
    Zhang LZ (2009) Biological data mining software development and its application in the research of prostate cancer diagnosis. Sun Yat-sen Univ 24:21–37Google Scholar
  22. 22.
    Chen SZ, Ma WL, Zheng WL (2007) Data mining for cervical cancer associated gene expressed sequence tags from gene chip data. Prog Mod Biomed 7(12):1800–1803Google Scholar
  23. 23.
    Zhou LP (2007) Hierarchical clustering of lung cancer-related genes. Sun Yat-sen Univ 35:20–32Google Scholar
  24. 24.
    Zhang DK (2009) Study on prognostic factors in stage IIA squamous cell carcinoma of esophagus based on molecular information and data mining techniques. Sun Yat-sen Univ 46:280–282Google Scholar
  25. 25.
    Liu X, Liao ZF, Fan XP (2005) Decision tree—based diagnostic system of early colorectal carcinoma. Chin Med Eng 13(5):462–465Google Scholar
  26. 26.
    Liao ZF, Fan XP, Chen YZ (2008) Research on new classification algorithm for colorectal carcinoma diagnosis data. Comput Eng Appl 44(20):208–211Google Scholar
  27. 27.
    Smith FM, Gallagher WM, Fox E, Stephens RB, Rexhepaj E, Petricoin EF, Liotta L, Kennedy MJ, Reynolds JV (2007) Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy. Ann Surg 245(2):259–266Google Scholar
  28. 28.
    Chhieng D et al (2007) Predicting 5 year survival of colorectal carcinoma patients using data mining methods. AMIA symposium proceedings, vol 21, 907–912Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of Medical Information EngineeringGuangzhou University of Chinese MedicineGuangzhouChina
  2. 2.Department of AcupunctureThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina

Personalised recommendations