Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction

Fariba Shadabi, Dharmendra Sharma, Robert Cox

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    6 Citations (Scopus)

    Abstract

    Predicting the outcome of a medical procedure or event with high level of accuracy can be a challenging task. To answer the challenge, data mining can play a significant role. The main objective of this study is to examine the performances of an artificially intelligent (Al)-based data mining technique namely artificial neural network ensemble (ANNE) in prediction of medical outcomes. It also describes a novel approach, namely "RIDC-ANNE". This approach tries to improve data quality by configuring an ensemble of bagged networks as a filter and identifying the regions in the data space that have high impact on the system performance. Furthermore, it can also be used to extract explanations and knowledge from several combined neural network classifiers. The methodology employed utilizes a series of clinical datasets. The datasets embody a number of important properties, which make them a good starting point for the purpose of this research. This study reveals that the RIDC-ANNE approach can be used to successfully extract the regions in the data space that have high impact on the system performance and enhance the overall utility of current neural network models
    Original languageEnglish
    Title of host publication2006 Innovations in Information Technology
    EditorsGeorge J Sun
    Place of PublicationFinland
    PublisherAcademy Publisher
    Pages1-5
    Number of pages5
    ISBN (Print)1-4244-0674-9, 9781424406739
    DOIs
    Publication statusPublished - 2006
    EventInnovations in Information Technology Conference, 2006 - Dubai, United Arab Emirates
    Duration: 19 Nov 200621 Nov 2006

    Conference

    ConferenceInnovations in Information Technology Conference, 2006
    CountryUnited Arab Emirates
    CityDubai
    Period19/11/0621/11/06

    Cite this

    Shadabi, F., Sharma, D., & Cox, R. (2006). Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. In G. J. Sun (Ed.), 2006 Innovations in Information Technology (pp. 1-5). Finland: Academy Publisher. https://doi.org/10.1109/INNOVATIONS.2006.301896
    Shadabi, Fariba ; Sharma, Dharmendra ; Cox, Robert. / Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. 2006 Innovations in Information Technology. editor / George J Sun. Finland : Academy Publisher, 2006. pp. 1-5
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    Shadabi, F, Sharma, D & Cox, R 2006, Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. in GJ Sun (ed.), 2006 Innovations in Information Technology. Academy Publisher, Finland, pp. 1-5, Innovations in Information Technology Conference, 2006, Dubai, United Arab Emirates, 19/11/06. https://doi.org/10.1109/INNOVATIONS.2006.301896

    Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. / Shadabi, Fariba; Sharma, Dharmendra; Cox, Robert.

    2006 Innovations in Information Technology. ed. / George J Sun. Finland : Academy Publisher, 2006. p. 1-5.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - Cox, Robert

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    N2 - Predicting the outcome of a medical procedure or event with high level of accuracy can be a challenging task. To answer the challenge, data mining can play a significant role. The main objective of this study is to examine the performances of an artificially intelligent (Al)-based data mining technique namely artificial neural network ensemble (ANNE) in prediction of medical outcomes. It also describes a novel approach, namely "RIDC-ANNE". This approach tries to improve data quality by configuring an ensemble of bagged networks as a filter and identifying the regions in the data space that have high impact on the system performance. Furthermore, it can also be used to extract explanations and knowledge from several combined neural network classifiers. The methodology employed utilizes a series of clinical datasets. The datasets embody a number of important properties, which make them a good starting point for the purpose of this research. This study reveals that the RIDC-ANNE approach can be used to successfully extract the regions in the data space that have high impact on the system performance and enhance the overall utility of current neural network models

    AB - Predicting the outcome of a medical procedure or event with high level of accuracy can be a challenging task. To answer the challenge, data mining can play a significant role. The main objective of this study is to examine the performances of an artificially intelligent (Al)-based data mining technique namely artificial neural network ensemble (ANNE) in prediction of medical outcomes. It also describes a novel approach, namely "RIDC-ANNE". This approach tries to improve data quality by configuring an ensemble of bagged networks as a filter and identifying the regions in the data space that have high impact on the system performance. Furthermore, it can also be used to extract explanations and knowledge from several combined neural network classifiers. The methodology employed utilizes a series of clinical datasets. The datasets embody a number of important properties, which make them a good starting point for the purpose of this research. This study reveals that the RIDC-ANNE approach can be used to successfully extract the regions in the data space that have high impact on the system performance and enhance the overall utility of current neural network models

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    Shadabi F, Sharma D, Cox R. Learning from Ensembles: Using Artificial Neural Network Ensemble for Medical Outcomes Prediction. In Sun GJ, editor, 2006 Innovations in Information Technology. Finland: Academy Publisher. 2006. p. 1-5 https://doi.org/10.1109/INNOVATIONS.2006.301896