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:
Fiber Bragg Based Sensors for Foot Plantar Pressure Analysis Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment

verfasst von : Juan L. Domínguez-Olmedo, Jacinto Mata, Victoria Pachón, Jose L. Lopez Guerra

Erschienen in: Biomedical Engineering Systems and Technologies

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

In this work we describe a rule-based classifier (DEQAR-CC), which employs a combination of selected rules after a two-phase training process, and without the need of a previous discretization for the numerical variables. It was compared in the application to a real imbalanced dataset regarding the toxicity during and after radiation therapy for prostate cancer. In this comparison with other predictive methods (rule-based, artificial neural networks, trees, Bayesian and logistic regression), DEQAR-CC showed a better global prediction performance than the rest of classifiers, in an evaluation regarding several performance measures and by using cross-validation. Finally, it was employed to obtain a predictive model for genitourinary toxicity, obtaining an interpretable classification scheme which simply combines two rules with two variables.

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 Exploring the Value of Electronic Health Records from Multiple Datasets
Nächstes Kapitel Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data
Literatur
1.
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD ICMD, pp. 207–216 (1993)CrossRef
2.
3.
4.
Grosskreutz, H., Rüping, S.: On subgroup discovery in numerical domains. Data Min. Knowl. Disc. 19(2), 210–226 (2009)MathSciNetCrossRef
5.
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009)CrossRef
6.
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
7.
Sun, Y., Wong, A.K.C., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recognit. Artif. Intell. 23(4), 687–719 (2009)CrossRef
8.
Domínguez-Olmedo, J.L., Mata, J., Pachón, V., Lopez Guerra, J.L.: A rule-based method applied to the imbalanced classification of radiation toxicity. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 5, HEALTHINF, pp. 147–155 (2018)
9.
Ferlay, J., et al.: Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur. J. Cancer 49(6), 1374–1403 (2013)CrossRef
10.
Domínguez-Olmedo, J.L., Mata Vázquez, J.: Comparison of standard discretization with a new method for quantitative association rules. IEEE Lat. Am. Trans. 14(4), 1879–1885 (2016)CrossRef
11.
12.
Domínguez-Olmedo, J.L., Mata Vázquez, J.: Obtaining significant and interpretable rules for subgroup discovery tasks. IEEE Lat. Am. Trans. 15(10), 2012–2016 (2017)CrossRef
13.
14.
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
15.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986)
16.
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
17.
18.
Pearl, J.: Bayesian networks: a model of self-activated memory for evidential reasoning. UCLA Computer Science Department (1985)
19.
John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338–345 (1995)
20.
Le-Cessie, S., van Houwelingen, J.C.: Ridge estimators in logistic regression. Appl. Stat. 41(1), 191–201 (1992)CrossRef
21.
Lopez Guerra, J.L., et al.: Ethnic difference in risk of toxicity in prostate cancer patients treated with dynamic arc radiation therapy. Tumori 101(4), 461–468 (2015)CrossRef
22.
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)CrossRef
23.
Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure 405(2), 442–451 (1975)CrossRef
24.
Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)MathSciNetCrossRef
25.
Frank, E., Hall, M.A., Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
26.
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetCrossRef
27.
Acevedo-Henao, C.M., Lopez, J.L., Matute, R., Azinovic, I.: Image-guided radiation therapy based on helical tomotherapy in prostate cancer: minimizing toxicity. Oncol. Res. Treat. 37(6), 324–330 (2014)CrossRef
28.
Ahmed, A.A., et al.: A novel method for predicting late genitourinary toxicity after prostate radiation therapy and the need for age-based risk-adapted dose constraints. Int. J. Radiat. Oncol. Biol. Phys. 86(4), 709–715 (2013)CrossRef
Metadaten
Titel
Application of a Rule-Based Classifier to Data Regarding Radiation Toxicity in Prostate Cancer Treatment
verfasst von
Juan L. Domínguez-Olmedo
Jacinto Mata
Victoria Pachón
Jose L. Lopez Guerra
Copyright-Jahr
2019
Buch
Biomedical Engineering Systems and Technologies

Print ISBN: 978-3-030-29195-2

Electronic ISBN: 978-3-030-29196-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-29196-9_20