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A neuro-fuzzy based healthcare framework for disease analysis and prediction

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Abstract

With the augmentation of computing and communication technologies, versatile and huge volume of distinguished data sources, the domain of healthcare evidences various promising use-cases among analytical community. Further, the healthcare systems contains disparate, complex and heterogeneous information resources. It prompts the need for applying novel techniques and computational models for reaping potential patterns of interest from the available healthcare data. This paper proposes a neuro-fuzzy based healthcare framework to preprocess the healthcare records and perform disease prediction. The framework constructs a layered approach for performing the task such as preprocessing of healthcare data, normalization through fuzzification process, disease prediction by applying appropriate rules, and de-fuzzification of output values towards obtaining information pertain to predicted disease. The fuzzy rule base is effectively designed to strengthen the decision process. The efficiency of the proposed system is validated with the experimental setup and compared with the fuzzy based and linguistic neuro-fuzzy with feature extraction models. The proposed neuro-fuzzy based method achieves the accuracy value of 87.7%, which is better than the existing methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai, Tamilnadu, India

    Rajganesh Nagarajan

  2. School of Information Technology & Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India

    Ramkumar Thirunavukarasu

Authors
  1. Rajganesh Nagarajan
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  2. Ramkumar Thirunavukarasu
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Correspondence to Rajganesh Nagarajan.

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Nagarajan, R., Thirunavukarasu, R. A neuro-fuzzy based healthcare framework for disease analysis and prediction. Multimed Tools Appl 81, 11737–11753 (2022). https://doi.org/10.1007/s11042-022-12369-2

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