Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
From Literature to Knowledge: Exploiting PubMed to Answer Biomedical Questions in Natural Language Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Data Mining Techniques in Health Informatics: A Case Study from Breast Cancer Research

Authors : Jing Lu, Alan Hales, David Rew, Malcolm Keech, Christian Fröhlingsdorf, Alex Mills-Mullett, Christian Wette

Published in: Information Technology in Bio- and Medical Informatics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka (Waikato Environment for Knowledge Analysis). In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction with ST-Segment Elevation
next chapter Artificial Neural Networks in Diagnosis of Liver Diseases
Literature
1.
Burke, H.B., Rosen, D., Goodman, P.: Comparing the prediction accuracy of artificial neural networks and other statistical models for breast cancer survival. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, pp. 1063–1068. MIT Press, Cambridge (1995)
2.
Campbell, K., Thygeson, N.N., Srivastava, J., Speedie, S.: Exploration of Classification Techniques as a Treatment Decision Support Tool for Patients with Uterine Fibroids. In: International Workshop on Data Mining for HealthCare Management, PAKDD (2010)
3.
Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)CrossRef
4.
Fayyad, U., PiatetskyShapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine. 17(3), 37–54 (1996)
5.
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann. (2011)
6.
Jacob, S.G., Ramani, R.G.: Data mining in clinical data sets: a review. Int. J. Appl. Inf. Syst. 4(6), 15–16 (2012)
7.
Jerez-Aragones, J.M., Gomez-Ruiz, J.A., Ramos-Jimenez, G., MunozPerez, J., Alba-Conejo, E.: A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif. Intell. Med. 27(1), 45–63 (2003)CrossRef
8.
Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell. Inform. Bull. 15(1), 6–14 (2014)
9.
Laxminarayan, P., Alvarez, S.A., Ruiz, C., Moonis, M.: Mining statistically significant associations for exploratory analysis of human sleep data. IEEE Trans. Inf Technol. Biomed. 10(3), 440–450 (2006)CrossRef
10.
Lee, Y.J., Mangasarian, O.L., Wolberg, W.H.: Survival-time classification of breast cancer patients. Comput. Optim. Appl. 25(1–3), 151–166 (2003)MathSciNetCrossRef
11.
Li, Q., Feng, J., Wang, L., Chu, H., Yu, H.: Method for knowledge acquisition and decision-making process analysis in clinical decision support system. In: Bursa, M., Khuri, S., Renda, M. (eds.) ITBAM 2014. LNCS, vol. 8649, pp. 79–82. Springer, Heidelberg (2014)
12.
Lu, J., Chen, W.R., Adjei, O., Keech, M.: Sequential patterns post-processing for structural relation patterns mining. Int. J. Data Warehousing and Mining 4(3), 71–89 (2008). IGI Global, Hershey, PennsylvaniaCrossRef
13.
Mahajan, R., Shneiderman, B.: Visual and textual consistency checking tools for graphical user interfaces. IEEE Trans. Software Eng. 23(11), 722–735 (1997)CrossRef
14.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–11 (2009)CrossRef
15.
Martin, M.A., Meyricke, R., O’Neill, T., Roberts, S.: Mastectomy or breast conserving surgery? factors affecting type of surgical treatment for breast cancer: a classification tree approach. BMC Cancer 6, 98 (2006)CrossRef
16.
Quinlan, J. Ross. C4.5: Programs for Machine Learning. Elsevier (2014)
17.
Razavi, A.R., Gill, H., Ahlfeldt, H., Shahsavar, N.: Predicting metastasis in breast cancer: comparing a decision tree with domain experts. J. Med. Syst. 31, 263–273 (2007)CrossRef
18.
Reps, J., Garibaldi, J.M., Aickelin, U., Soria, D., Gibson, J.E., Hubbard, R.B.: Discovering Sequential Patterns in a UK General Practice Database. In: IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 960–963 (2012)
19.
Rew, D.A.: Understanding outcomes in cancer surgery through time structured patient records. Indian J. Surg. Oncol. 2(4), 265–270 (2011)CrossRefMATH
20.
Stolba, N., Tjoa, A.: The relevance of data warehousing and data mining in the field of evidence-based medicine to support healthcare decision making. Int. J. Comput. Syst. Sci. Eng. 3(3), 143–148 (2006)
Metadata
Title
Data Mining Techniques in Health Informatics: A Case Study from Breast Cancer Research
Authors
Jing Lu
Alan Hales
David Rew
Malcolm Keech
Christian Fröhlingsdorf
Alex Mills-Mullett
Christian Wette
Copyright Year
2015
Book
Information Technology in Bio- and Medical Informatics

Print ISBN: 978-3-319-22740-5

Electronic ISBN: 978-3-319-22741-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22741-2_6

Premium Partner

    Image Credits