Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Green Supply Chain: An ISM-Based Roadmap to Boundaries of Environmental Sustainability Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

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

verfasst von : Neha Sharma, Hari Om

Erschienen in: Systems Thinking Approach for Social Problems

Verlag: Springer India

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Development of Green Manufacturing System in Indian Apparel Industry
Nächstes Kapitel Admission Control in Communication Networks Using Discrete Time Sliding Mode Control
Literatur
1.
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, Massachusetts. ISBN 978-0123814791
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 36:634–655CrossRef
3.
Hen LE (2008) Performance analysis of data mining tools cumulating with a proposed data mining middleware. J Comput Sci 4: 26
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
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
6.
Data Mining Curriculum. ACM SIGKDD. May 30, 2006
7.
Clifton C, (2010) Encyclopedia britannica: definition of data mining
8.
Hastie T, Tibshirani R, Friedman (2009) The elements of statistical learning: data mining, inference, and prediction
9.
Coelho KR (2012) Challenges in oral cancer burden in India. J Cancer Epidemiol 701932:17
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
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
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
13.
Cong G, Liu B, (2002) Speed-up iterative frequent itemset mining with constraint changes. ICDM. pp 107–114
14.
Lee AJ, Lin WC, Wang CS (2006) Mining association rule with multi-dimensional constraints. J Syst Softw 79:79–92
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
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
17.
Swami S et al (2011) Multidimensional association rules extraction in smoking habit database. Int J Adv Net Appl 03(03):1176–1179
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
19.
Srikant R, Vu Q Agrawal R (1997) Mining association rules with item constraints. In: Proceeding KDD97, pp 67–73
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
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
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
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
25.
Gadewal NS, Zingde SM (2011) Database and interaction network of genes involved in oral cancer: version II. Bioinformation 06(04):169–170CrossRef
26.
Werning JW (2007) Oral cancer: diagnosis, management, and rehabilitation. 16 May 2007, p 1. ISBN 978-1588903099
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)CrossRef
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
29.
Warnakulasuriya S (2009) Global epidemiology of oral and oropharyngeal cancer. Oral Oncol 45(4):309–316
30.
31.
Diagnosis and management of head and neck cancer, Scottish Intercollegiate Guidelines Network–SIGN, 2006
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):1843–1849CrossRef
33.
Gosselin EJ, Meyers AD (eds) (2011) Malignant tumors of the mobile tongue. Medscape
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
35.
An J, Chen YPP, Chen H (2005) DDR: An Index method for large time series datasets. Inf Syst 30:333–348CrossRef
36.
Chen YPP, Chen F (2008) Targets for drug discovery using bioinformatics. Expert Opin Targets. 12(04):383–389CrossRef
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):41–72CrossRefMATH
38.
Ordonez C (2006) Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans Inf Technol Biomed 10(02):334–343CrossRef
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
40.
Ordonez C, Santana CA, Braal L (2000) Discovering interesting association rules in medical data. ACM DMKD Workshop, pp 78–85
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
42.
43.
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
45.
Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, pp 229–248
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
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
48.
Witten IH, Frank E (2005) Data Mining: practical machine learning tool and techniques, 2nd edn. Morgan Kaufmann Publishers, Elsevier, San Francisco
Metadaten
Titel
Significant Patterns Extraction to Find Most Effective Treatment for Oral Cancer Using Data Mining
verfasst von
Neha Sharma
Hari Om
Copyright-Jahr
2015
Verlag
Springer India
Buch
Systems Thinking Approach for Social Problems

Print ISBN: 978-81-322-2140-1

Electronic ISBN: 978-81-322-2141-8

Copyright-Jahr: 2015

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