Skip to main content
SpringerLink
Log in

Significant Patterns Extraction to Find Most Effective Treatment for Oral Cancer Using Data Mining

  • Conference paper
  • First Online:
Systems Thinking Approach for Social Problems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 327))

Abstract

Development of cancer in oral mucosa as classified by the World Health Organization is a two-stage process that initially shows up as a premalignant, precancerous sore and that subsequently develops into the malignant cancerous stage. Early evaluation of oral precancerous lesions has a dramatic impact on oral cancer mortality rates as the medicine is very effective in early stage diagnosis. This paper aims at extracting the patterns that help finding the most effective course of oral cancer treatment and its post-treatment management. The Apriori algorithm is used to mine a set of significant rules for prevention of oral cancer by adopting the most efficient treatment. We attempt to find the association among various treatments, histopathology, follow-up symptoms, and follow-up examination. The experimental results show that all the generated rules hold the highest confidence level, thereby making them very useful for deciding effective treatment to cure oral cancer and its follow-up.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, Massachusetts. ISBN 978-0123814791

    Google Scholar 

  2. Shital CS, Andrew K, Michael A, Donnell O (2006) Patient-recognition data mining model for BCG-plus interferon immunotherapy bladder cancer treatment. Comput Biol Med 36:634–655

    Article  Google Scholar 

  3. Hen LE (2008) Performance analysis of data mining tools cumulating with a proposed data mining middleware. J Comput Sci 4: 26

    Google Scholar 

  4. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. Am Assoc Artif Intell (AAAI-AI Magazine), pp 37–54

    Google Scholar 

  5. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. Advances in knowledge discovery and data mining. AAAI Press/MIT Press, Cambridge, pp 1–36

    Google Scholar 

  6. Data Mining Curriculum. ACM SIGKDD. May 30, 2006

    Google Scholar 

  7. Clifton C, (2010) Encyclopedia britannica: definition of data mining

    Google Scholar 

  8. Hastie T, Tibshirani R, Friedman (2009) The elements of statistical learning: data mining, inference, and prediction

    Google Scholar 

  9. Coelho KR (2012) Challenges in oral cancer burden in India. J Cancer Epidemiol 701932:17

    Google Scholar 

  10. Singh S, Yadav M, Gupta H (2012) Finding the chances and prediction of cancer through Apriori algorithm with transaction reduction. Int J Adv Comput Res 2(2):23–28 ISSN (print): 2249-7277 ISSN (online): 2277-7970

    Google Scholar 

  11. Anh TN, Hai DV, Tin TC, Bac LH (2011) Efficient algorithms for mining frequent itemsets with constraint. In: Proceedings of the third international conference on knowledge and systems engineering

    Google Scholar 

  12. Bayardo RJ, Agrawal R, Gunopulos D (2000) Constraint-based rule mining in large, dense databases. Data Mining Knowl Disc 4(2–3):217–240 Kluwer Academic Publication

    Google Scholar 

  13. Cong G, Liu B, (2002) Speed-up iterative frequent itemset mining with constraint changes. ICDM. pp 107–114

    Google Scholar 

  14. Lee AJ, Lin WC, Wang CS (2006) Mining association rule with multi-dimensional constraints. J Syst Softw 79:79–92

    Google Scholar 

  15. Nguyen RT, Lakshman VS, Han J, Pang A (1998) Exploratory mining and pruning optimizations of constrained association rules. In: International conference on management of data, ACM-SIG-MOD pp 13–24

    Google Scholar 

  16. RuthRamya K et al (2012) A class based approach for medical classification of chest pain. Int J Eng Trends Technol 3(2):89–93

    Google Scholar 

  17. Swami S et al (2011) Multidimensional association rules extraction in smoking habit database. Int J Adv Net Appl 03(03):1176–1179

    Google Scholar 

  18. Ha SH, Joo SH (2010) A Hybrid data mining method for medical classification of chest pain. World Acad Sci Eng Technol 37:608–613

    Google Scholar 

  19. Srikant R, Vu Q Agrawal R (1997) Mining association rules with item constraints. In: Proceeding KDD97, pp 67–73

    Google Scholar 

  20. Nahar J, Kevin ST, Ali ABMS, Chen YP (2009) Significant cancer prevention factor extraction: an association rule discovery approach. J Med Syst. doi:10.1007/s10916-009-9372-8

  21. Milovic B, Milovic M (2012) Prediction and decision making in health care using data mining. Int J Public Health Sci 01(02):69–78

    Google Scholar 

  22. Anuradha K, Sankaranarayanan K (2012) Identification of suspicious regions to detect Oral cancers at an earlier stage—a literature survey. Int J Adv Eng Technol 03(01):84–91

    Google Scholar 

  23. Kaladhar DSVGK, Chandana B, Kumar PB (2011) Predicting cancer survivability using classification algorithms. Int J Res Rev Comput Sci (IJRRCS) 02(02):340–343

    Google Scholar 

  24. Chuang LY, Wu KC, Chang HW, Yang CH (2011) Support vector machine-based prediction for Oral cancer using four SNPS in DNA repair genes. In: Proceedings of the international multi conference of engineers and computer scientists, 16–18 March, 2011

    Google Scholar 

  25. Gadewal NS, Zingde SM (2011) Database and interaction network of genes involved in oral cancer: version II. Bioinformation 06(04):169–170

    Article  Google Scholar 

  26. Werning JW (2007) Oral cancer: diagnosis, management, and rehabilitation. 16 May 2007, p 1. ISBN 978-1588903099

    Google Scholar 

  27. Scully C, Bagan JV (2009) Recent advances in oral oncology 2008; squamous cell carcinoma imaging, treatment, prognostication and treatment outcomes. Oral Oncol 45(6):e25–e30 (Epub 26 Feb 2009)

    Article  Google Scholar 

  28. SA Barbellido, Trapero JC, Sanchez CJ et al (2008) Gene therapy in the management of oral cancer: review of the literature. Med Oral Patol Oral Cir Bucal 13(1):E15–E21

    Google Scholar 

  29. Warnakulasuriya S (2009) Global epidemiology of oral and oropharyngeal cancer. Oral Oncol 45(4):309–316

    Google Scholar 

  30. www.cancerresearchuk.org/cancerinfo/cancerstats/types/oral/incidence/ukoral-cancer-incidence-statistics

  31. Diagnosis and management of head and neck cancer, Scottish Intercollegiate Guidelines Network–SIGN, 2006

    Google Scholar 

  32. Shiboski CH, Schmidt BL, Jordan RC (2005) Tongue and tonsil carcinoma: increasing trends in the U.S. population ages 20–44 years. Cancer 103(9):1843–1849

    Article  Google Scholar 

  33. Gosselin EJ, Meyers AD (eds) (2011) Malignant tumors of the mobile tongue. Medscape

    Google Scholar 

  34. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, pp 207–216

    Google Scholar 

  35. An J, Chen YPP, Chen H (2005) DDR: An Index method for large time series datasets. Inf Syst 30:333–348

    Article  Google Scholar 

  36. Chen YPP, Chen F (2008) Targets for drug discovery using bioinformatics. Expert Opin Targets. 12(04):383–389

    Article  Google Scholar 

  37. Lau RYK, Tang M, Wong O, Milliner SW, Chen YPP (2006) An evolutionary learning approach for adaptive negotiation agents. Int J Intell Syst 21(01):41–72

    Article  MATH  Google Scholar 

  38. Ordonez C (2006) Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans Inf Technol Biomed 10(02):334–343

    Article  Google Scholar 

  39. Ordonez C, Omiecinski E (1999) Discovering association rules based on image content. In: IEEE advances in digital libraries conference (ADL’99), pp 38–49

    Google Scholar 

  40. Ordonez C, Santana CA, Braal L (2000) Discovering interesting association rules in medical data. ACM DMKD Workshop, pp 78–85

    Google Scholar 

  41. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, VLDB, pp 487–499

    Google Scholar 

  42. Zaki MJ (2004) Mining non-redundant association rules. Data Min Knowl Disc 09:223–248

    Article  MathSciNet  Google Scholar 

  43. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining—a general survey and comparison. ACM SIGKDD Explorations Newsl 2:58. doi:10.1145/360402.360421

    Article  Google Scholar 

  44. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD 1997), Tucson, Arizona, May 1997, 265–276

    Google Scholar 

  45. Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, pp 229–248

    Google Scholar 

  46. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD 1997), Tucson, Arizona, May 1997, pp 255–264

    Google Scholar 

  47. Sharma N, Om Hari (2012) Framework for early detection and prevention of oral cancer using data mining. Int. J Adv Eng Technol 4(2):302–310

    Google Scholar 

  48. Witten IH, Frank E (2005) Data Mining: practical machine learning tool and techniques, 2nd edn. Morgan Kaufmann Publishers, Elsevier, San Francisco

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Vijay Sharma, MS, ENT, for his valuable contribution in understanding the occurrence and diagnosis of oral cancer. The authors devote their sincere thanks to the management and staff of Indian School of Mines for their constant support and motivation.

Author information

Authors and Affiliations

  1. Padmashree Dr. D.Y. Patil Institute of Master of Computer Applications, Akurdi, Pune, 411044, Maharashtra, India

    Neha Sharma

  2. University of Pune, Pune, Maharashtra, India

    Neha Sharma

  3. Computer Science and Engineering Department, Indian School of Mines, Dhanbad, Jharkhand, India

    Hari Om

Authors
  1. Neha Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hari Om
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neha Sharma .

Editor information

Editors and Affiliations

  1. Systems Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India

    Vivek Vijay

  2. Information and Communication Tech., Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India

    Sandeep Kumar Yadav

  3. Systems Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India

    Bibhas Adhikari

  4. Systems Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India

    Harinipriya Seshadri

  5. Systems Science, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India

    Deepak Kumar Fulwani

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer India

About this paper

Cite this paper

Sharma, N., Om, H. (2015). Significant Patterns Extraction to Find Most Effective Treatment for Oral Cancer Using Data Mining. In: Vijay, V., Yadav, S., Adhikari, B., Seshadri, H., Fulwani, D. (eds) Systems Thinking Approach for Social Problems. Lecture Notes in Electrical Engineering, vol 327. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2141-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2141-8_33

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2140-1

  • Online ISBN: 978-81-322-2141-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation