IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v162y2005i2p532-551.html
   My bibliography  Save this article

Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application

Author

Listed:
  • West, David
  • Mangiameli, Paul
  • Rampal, Rohit
  • West, Vivian

Abstract

No abstract is available for this item.

Suggested Citation

  • West, David & Mangiameli, Paul & Rampal, Rohit & West, Vivian, 2005. "Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application," European Journal of Operational Research, Elsevier, vol. 162(2), pages 532-551, April.
    • Handle: RePEc:eee:ejores:v:162:y:2005:i:2:p:532-551
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(03)00741-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William G. Baxt, 1994. "A Neural Network Trained to Identify the Presence of Myocardial Infarction Bases Some Decisions on Clinical Associations That Differ from Accepted Clinical Teaching," Medical Decision Making, , vol. 14(3), pages 217-222, August.
    2. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    3. Olvi L. Mangasarian & W. Nick Street & William H. Wolberg, 1995. "Breast Cancer Diagnosis and Prognosis Via Linear Programming," Operations Research, INFORMS, vol. 43(4), pages 570-577, August.
    4. , 1998. "Predicting Mortality after Coronary Artery Bypass Surgery," Medical Decision Making, , vol. 18(2), pages 229-235.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. János Tóth & Henrietta Tomán & Gabriella Hajdu & András Hajdu, 2023. "Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    2. Salonee Patel & Manan Shah, 2023. "A Comprehensive Study on Implementing Big Data in the Auditing Industry," Annals of Data Science, Springer, vol. 10(3), pages 657-677, June.
    3. Yi Du & Hua Yu & Zhijun Li, 0. "Research of SVM ensembles in medical examination scheduling," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.
    4. Tom Lindström & Michael Tildesley & Colleen Webb, 2015. "A Bayesian Ensemble Approach for Epidemiological Projections," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-30, April.
    5. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    6. Wang, Fan & Zhang, Shengfan & Henderson, Louise M., 2018. "Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model," Omega, Elsevier, vol. 76(C), pages 70-84.
    7. Abellán, Joaquín & Masegosa, Andrés R., 2010. "An ensemble method using credal decision trees," European Journal of Operational Research, Elsevier, vol. 205(1), pages 218-226, August.
    8. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    9. Yi Du & Hua Yu & Zhijun Li, 2021. "Research of SVM ensembles in medical examination scheduling," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 1042-1052, November.
    10. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    2. Yaqiong Cui & Jukka Sirén & Timo Koski & Jukka Corander, 2016. "Simultaneous Predictive Gaussian Classifiers," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 73-102, April.
    3. Stephen G. Pauker & John B. Wong, 2005. "The Influence of Influence Diagrams in Medicine," Decision Analysis, INFORMS, vol. 2(4), pages 238-244, December.
    4. Palocsay, Susan W. & Wang, Ping & Brookshire, Robert G., 2000. "Predicting criminal recidivism using neural networks," Socio-Economic Planning Sciences, Elsevier, vol. 34(4), pages 271-284, December.
    5. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    6. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
    7. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    8. Sung, Bongjung & Lee, Jaeyong, 2023. "Covariance structure estimation with Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    9. Wang, Wan-Lun, 2015. "Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 223-235.
    10. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    11. Jun-Ya Gotoh & Michael Jong Kim & Andrew E. B. Lim, 2017. "Calibration of Distributionally Robust Empirical Optimization Models," Papers 1711.06565, arXiv.org, revised May 2020.
    12. Michel H. Montoril & Woojin Chang & Brani Vidakovic, 2019. "Wavelet-Based Estimation of Generalized Discriminant Functions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 318-349, December.
    13. Giovanni Felici & Klaus Truemper, 2002. "A MINSAT Approach for Learning in Logic Domains," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 20-36, February.
    14. Vijayalakshmi S & John A & Sunder R & Senthilkumar Mohan & Sweta Bhattacharya & Rajesh Kaluri & Guang Feng & Usman Tariq, 2020. "Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.
    15. David González-Patiño & Yenny Villuendas-Rey & Magdalena Saldaña-Pérez & Amadeo-José Argüelles-Cruz, 2023. "A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification," IJERPH, MDPI, vol. 20(4), pages 1-13, February.
    16. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    17. W. Art Chaovalitwongse & Ya-Ju Fan & Rajesh C. Sachdeo, 2008. "Novel Optimization Models for Abnormal Brain Activity Classification," Operations Research, INFORMS, vol. 56(6), pages 1450-1460, December.
    18. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    19. Ramazan Ünlü & Petros Xanthopoulos, 2019. "A weighted framework for unsupervised ensemble learning based on internal quality measures," Annals of Operations Research, Springer, vol. 276(1), pages 229-247, May.
    20. Morris, Katherine & McNicholas, Paul D., 2016. "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 133-150.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:162:y:2005:i:2:p:532-551. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.