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WebMAC: A web based clinical expert system

Author

Listed:
  • Saba Bashir

    (National University of Sciences and Technology (NUST))

  • Usman Qamar

    (National University of Sciences and Technology (NUST))

  • Farhan Hassan Khan

    (National University of Sciences and Technology (NUST))

Abstract

Disease diagnosis at early stages can enable the physicians to overcome the complications and treat them properly. The diagnosis method plays an important role in disease diagnosis and accuracy of its treatment. A diagnosis expert system can help a great deal in identifying those diseases and describing methods of treatment to be carried out; taking into account the user capability in order to deal and interact with expert system easily and clearly. A good way to improve diagnosis accuracy of expert systems is use of ensemble classifiers. The proposed research presents an expert system using multi-layer classification with enhanced bagging and optimized weighting. The proposed method is named as “M2-BagWeight” which overcomes the limitations of individual as well as other ensemble classifiers. Evaluation of the proposed model is performed on two different liver disease datasets, chronic kidney disease dataset, heart disease dataset, diabetic retinopathy debrecen dataset, breast cancer dataset and primary tumor dataset obtained from UCI public repository. It is clear from the analysis of results that proposed expert system has achieved high classification and prediction accuracy when compared with individual as well as ensemble classifiers. Moreover, an application named “WebMAC” is also developed for practical implementation of proposed model in hospital for diagnostic advice.

Suggested Citation

  • Saba Bashir & Usman Qamar & Farhan Hassan Khan, 2018. "WebMAC: A web based clinical expert system," Information Systems Frontiers, Springer, vol. 20(5), pages 1135-1151, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-016-9718-y
    DOI: 10.1007/s10796-016-9718-y
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    References listed on IDEAS

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    1. Chang-Gyu Yang & Hee-Jun Lee, 2016. "A study on the antecedents of healthcare information protection intention," Information Systems Frontiers, Springer, vol. 18(2), pages 253-263, April.
    2. Dong-Yop Oh & J. Gray, 2013. "GA-Ensemble: a genetic algorithm for robust ensembles," Computational Statistics, Springer, vol. 28(5), pages 2333-2347, October.
    3. Prem Timsina & Jun Liu & Omar El-Gayar, 2016. "Advanced analytics for the automation of medical systematic reviews," Information Systems Frontiers, Springer, vol. 18(2), pages 237-252, April.
    4. Atreyi Kankanhalli & Jungpil Hahn & Sharon Tan & Gordon Gao, 2016. "Big data and analytics in healthcare: Introduction to the special section," Information Systems Frontiers, Springer, vol. 18(2), pages 233-235, April.
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