Interdependent Attribute Interference Fuzzy Neural Network-Based Alzheimer Disease Evaluation

Syed Thouheed Ahmed, Manjula Sanjay Koti, V. Muthukumaran, Rose Bindu Joseph and Satheesh Kumar S.
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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

International Journal of Fuzzy System Applications (IJFSA), 2022, vol. 11, issue 3, 1-13

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%.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jfsa00:v:11:y:2022:i:3:p:1-13

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