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Parallel and Distributed Population based Feature Selection Framework for Health Monitoring

Author

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  • Naoual El Aboudi

    (Mohammed V University in Rabat, Rabat, Morocco)

  • Laila Benhlima

    (Mohammed V University in Rabat, Rabat, Morocco)

Abstract

Smart health monitoring systems have become the subject of an extensive research during the past decades due to their role in improving the quality of health care services. With the increase of heterogeneous data produced by these systems, traditional data preprocessing methods are not able to extract relevant information. Indeed, feature selection is a key phase to preprocess data, it aims to select a relevant feature subset to reach better classification results with an affordable computational cost. In this study, we provide an overview of existing feature selection methods especially those used in the context of Bigdata, pointing out their advantages and drawbacks. Then, we propose a parallel population based feature selection framework for health monitoring.

Suggested Citation

  • Naoual El Aboudi & Laila Benhlima, 2017. "Parallel and Distributed Population based Feature Selection Framework for Health Monitoring," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 7(1), pages 57-71, January.
    • Handle: RePEc:igg:jcac00:v:7:y:2017:i:1:p:57-71
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    Cited by:

    1. Ankit Kumar Jain & B. B. Gupta, 2018. "Towards detection of phishing websites on client-side using machine learning based approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(4), pages 687-700, August.
    2. Jolyane, Blouin-Bougie & Nabil, Amara, 2023. "Breast cancer risk prediction models’ adoption by Canadian providers - an in-depth qualitative comparative analysis," Journal of Business Research, Elsevier, vol. 157(C).

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