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An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

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Abstract

Online prediction of risk on breast cancer is a challenging task in the area of health care during the past decade. Since the existing statistical and data mining methods have limitations with respect to the prediction of breast cancer, there is a need for proposing more effective predictive models which can predict the breast cancer more effectively. In this paper, we propose a new intelligent online risk prediction model for predicting the breast cancer using fuzzy temporal rules more accurately. Moreover, this intelligent system determines the contributing attributes from the dataset using intelligent fuzzy temporal rules and also performs prediction by applying fuzzy rule-based classification with temporal constraints. Moreover, the rules are validated using a domain expert and the experiments conducted in this work using questionnaire, rule-based classification and consultation with domain expert have proved that the proposed system provides more accurate results for risk prediction than the other existing systems.

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Acknowledgements

The authors thank Dr. MohamadGouse M.S. and Dr. Nafeesa. Banu M.D. for providing the expert advice forming fuzzy rules pertaining to medical diagnosis.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering Giundy, Anna University, Chennai, Tamilnadu, 600025, India

    U. Kanimozhi & D. Manjula

  2. School of Computing Science and Engineering, VIT University, Chennai, Tamilnadu, 600127, India

    S. Ganapathy

  3. Department of Information Science and Technology, College of Engineering Giundy, Anna University, Chennai, Tamilnadu, 600025, India

    A. Kannan

Authors
  1. U. Kanimozhi
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  2. S. Ganapathy
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  3. D. Manjula
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  4. A. Kannan
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Corresponding author

Correspondence to U. Kanimozhi.

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Kanimozhi, U., Ganapathy, S., Manjula, D. et al. An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules. Natl. Acad. Sci. Lett. 42, 227–232 (2019). https://doi.org/10.1007/s40009-018-0732-0

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  • DOI: https://doi.org/10.1007/s40009-018-0732-0

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