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An effective clinical decision support system using swarm intelligence

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Abstract

As healthcare organizations collect a large volume of data on a daily basis, there is an absolute necessity to extract valuable information from them, owing to the importance and the time-sensitiveness of the industry. Although the healthcare sector has come up with several new computer technologies, the industry actually lags for an efficient approach to medical diagnosis. Hence, performing an accurate prediction of the patients’ medical problem through the use of an effective automated system comes in place. As in the recent survey, most of the medical research has explored predictive analytics for its performance efficiency; the proposed work uses the same for effective learning of medical data. The work proposes a novel filter-based feature selection method using a variant of a meta-heuristic search strategy. The experimental results show that the method is comparatively better than the existing filter-based feature selection methods and exclusively handles the imbalanced medical datasets using newly devised fitness functions.

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Acknowledgements

The authors would like to thank the doctors/domain experts (Dr. Bharathi and Dr. Anbumani) for providing their valuable opinion on the important features that could be selected for the diagnosis of diseases as mentioned in the experimental section of the proposed research. The first author would like to thank “Visvesvaraya Ph.D. Scheme, Meity, New Delhi” for supporting the proposed research work financially in the form of scholarship.

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Authors and Affiliations

  1. Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, India

    Vanaja Ramaswamy & Saswati Mukherjee

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  1. Vanaja Ramaswamy
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  2. Saswati Mukherjee
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Correspondence to Vanaja Ramaswamy.

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Ramaswamy, V., Mukherjee, S. An effective clinical decision support system using swarm intelligence. J Supercomput 76, 6599–6618 (2020). https://doi.org/10.1007/s11227-019-02888-5

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