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Interdependent Attribute Interference Fuzzy Neural Network-Based Alzheimer Disease Evaluation

Author

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  • Syed Thouheed Ahmed

    (REVA University, India)

  • Manjula Sanjay Koti

    (Dayananda Sagar Academy of Technology and Management, India)

  • V. Muthukumaran

    (REVA University, India)

  • Rose Bindu Joseph

    (Christ Academy Institute for Advanced Studies, India)

  • Satheesh Kumar S.

    (REVA University, India)

Abstract

Alzheimer’s disease is associated with a fragmental protein deposits termed as biomarkers. These biomarkers are studied and researched with various techniques in improving the performance and accuracy of diagnosis. In this research article, a technique is proposed to extract the attribute of brain MRI datasets. The attributes are processed and computed using a neural networking technique to categorize attribute mapping based on Interdependent Attribute Interference (IAI). The categorized data is teamed with a fuzzy logic to provide a reliable computation rule in decision making. The proposed technique has outperformed the accuracy of disease evaluation and diagnosis with a categorization sensitivity of 89.27% and an accuracy of 93.91%.

Suggested Citation

  • Syed Thouheed Ahmed & Manjula Sanjay Koti & V. Muthukumaran & Rose Bindu Joseph & Satheesh Kumar S., 2022. "Interdependent Attribute Interference Fuzzy Neural Network-Based Alzheimer Disease Evaluation," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(3), pages 1-13, July.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:3:p:1-13
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