Neural Computing and Applications

, Volume 24, Issue 6, pp 1311–1316 | Cite as

Application of machine learning to predict the recurrence-proneness for cervical cancer

  • Chih-Jen Tseng
  • Chi-Jie Lu
  • Chi-Chang Chang
  • Gin-Den Chen
Original Article


This study applied advanced machine learning techniques, widely considered as the most successful method to produce objective to an inferential problem of recurrent cervical cancer. Traditionally, clinical diagnosis of recurrent cervical cancer was based on physician’s clinical experience with various risk factors. Since the risk factors are broad categories, years of clinical study and experience have tried to identify key risk factors for recurrence. In this study, three machine learning approaches including support vector machine, C5.0 and extreme learning machine were considered to find important risk factors to predict the recurrence-proneness for cervical cancer. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrate that C5.0 model is the most useful approach to the discovery of recurrence-proneness factors. Our findings suggest that four most important recurrence-proneness factors were Pathologic Stage, Pathologic T, Cell Type and RT Target Summary. In particular, Pathologic Stage and Pathologic T were important and independent prognostic factor. To study the benefit of adjuvant therapy, clinical trials should randomize patients stratified by these prognostic factors, and to improve surveillance after treatment might lead to earlier detection of relapse, and precise assessment of recurrent status could improve outcome.


Recurrent cervical cancer Support vector machine Extreme learning machine C5.0 



This work is supported by the Chung Shan Medical University Hospital: CSH-2012-C-012.


  1. 1.
    Parkin DM, Bray FI, Devesa SS (2001) Cancer burden in the year 2000: the global picture. Eur J Cancer 37:S4–S66CrossRefGoogle Scholar
  2. 2.
    Goldie SJ, Kuhn L, Denny L, Pollack A, Wright T (2001) Policy analysis of cervical cancer screening strategies in low-resource setting: clinical benefits and cost effectiveness. J Am Med Assoc 285:3107–3115CrossRefGoogle Scholar
  3. 3.
    Delgado G, Bundy B, Zaino R, Sevin BU, Creasman WT, Major F (1990) Prospective surgical—pathological study of disease-free interval in patients with stage Ib squamous cell carcinoma of the cervix: a gynecologic oncology group study. Gynecol Oncol 38:352–357CrossRefGoogle Scholar
  4. 4.
    Lai CH, Hong JH, Hsueh S (1999) Preoperative prognostic variables and the impact of postoperative adjuvant therapy on the outcomes of stage IB or II cervical carcinoma patients with or without pelvic lymph node metastases. Cancer 85:1537–1546CrossRefGoogle Scholar
  5. 5.
    Waggoner SE (2003) Cervical cancer. Lancet 361:2217–2225CrossRefGoogle Scholar
  6. 6.
    Berek JS, Hacker NF (2005) Practical gynaecologic oncology. Lippincott Williams & Wilkins, New YorkGoogle Scholar
  7. 7.
    Kamura T, Tsukamoto N, Tsuruchi N, Saito T, Matsuyama T, Akazawa K (1992) Multivariate analysis of the histopathologic prognostic factors of cervical cancer in patients undergoing radical hysterectomy. Cancer 69:181–186CrossRefGoogle Scholar
  8. 8.
    Grisaru DA, Covens A, Fransen E, Chapman W, Shaw P, Colgan T (2003) Histopathologic score predicts recurrence free survival after radical surgery in patients with stage IA2-IB1–2 cervical carcinoma. Cancer 97:1904–1908CrossRefGoogle Scholar
  9. 9.
    Ho SH, Jee SH, Lee JE, Park JS (2004) Analysis on risk factors for cervical cancer using induction technique. Expert Syst Appl 27(1):97–105CrossRefGoogle Scholar
  10. 10.
    Thangavel K, Jaganathan PP, Easmi PO (2006) Data mining approach to cervical cancer patients analysis using clustering technique. Asian J Inf Technol 5(4):413–417Google Scholar
  11. 11.
    Louie KS, de Sanjose S, Mayaud P (2009) Epidemiology and prevention of human papillomavirus and cervical cancer in sub-Saharan Africa: a comprehensive review. Trop Med Int Health 14(10):1287–1302CrossRefGoogle Scholar
  12. 12.
    Kim HS, Park NH, Kang SB (2008) Rare metastases of recurrent cervical cancer to the pericardium and abdominal muscle. Arch Gynecol Obstet 278:479–482CrossRefGoogle Scholar
  13. 13.
    Kruppa J, Ziegler A, König IR (2012) Risk estimation and risk prediction using machine-learning methods. Hum Genet 131(10):1639–1654CrossRefGoogle Scholar
  14. 14.
    Hu YH, Wu F, Lo CL, Tai CT (2012) Predicting warfarin dosage from clinical data: a supervised learning approach. Artif Intell Med 56(1):27–34CrossRefGoogle Scholar
  15. 15.
    Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK (2011) Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif Intell Med 53(2):107–118CrossRefGoogle Scholar
  16. 16.
    Vapnik VN (2000) The nature of statistical learning theory. Springer, BerlinCrossRefzbMATHGoogle Scholar
  17. 17.
    Huang GR, Zhu QY, Siew CX (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  18. 18.
    Gomathi M, Thangaraj P (2011) A computer aided diagnosis system for lung cancer detection using machine learning technique. Eur J Sci Res 51:260–275Google Scholar
  19. 19.
    Malar E, Kandaswamy A, Chakravarthy D, Giri Dharan A (2012) A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine. Comput Biol Med 42:898–905CrossRefGoogle Scholar
  20. 20.
    Vanneschi L, Farinaccio A, Mauri G, Antoniotti M, Provero P, Giacobini M (2011) A comparison of machine learning techniques for survival prediction in breast cancer. BioData Mining 4(1):12CrossRefGoogle Scholar
  21. 21.
    Bharathi A, Natarajan AM (2012) Efficient classification of cancer using support vector machines and modified extreme learning machine based on analysis of variance features. Am J Appl Sci 8(12):1295–1301CrossRefGoogle Scholar
  22. 22.
    Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  23. 23.
    Larose DT (2005) Discovering knowledge in data: an introduction to data mining. Wiley, New JerseyGoogle Scholar
  24. 24.
    Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufinann, San MateoGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Chih-Jen Tseng
    • 1
    • 2
  • Chi-Jie Lu
    • 3
  • Chi-Chang Chang
    • 1
    • 4
  • Gin-Den Chen
    • 2
  1. 1.School of Medical InformaticsChung Shan Medical UniversityTaichungTaiwan, ROC
  2. 2.Department of Obstetrics and GynecologyChung Shan Medical University HospitalTaichungTaiwan, ROC
  3. 3.Department of Industrial ManagementChien Hsin University of Science and TechnologyZhongli CityTaiwan, ROC
  4. 4.IT OfficeChung Shan Medical University HospitalTaichungTaiwan, ROC

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