Multidimensional Data Analysis of The FMRI Attention Paradigm ‎Using Machine Learning and Web Tool Development for Interactive Knowledge Discovery

  • Authors

    • R . Manivannan Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana 500001, India.
    • Gavini Sreelatha Department of Information Technology, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana 500001, India.
    • Y V S Sai Pragathi Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana 500001, India.
    • P . Nagamani Department of Information Technology, Anurag University, Venkatapuram, Hyderabad, Telangana 500088, India
    https://doi.org/10.14419/7v3bz393

    Received date: April 18, 2025

    Accepted date: June 18, 2025

    Published date: June 30, 2025

  • Multidimensional Data Analysis; Machine Learning; Web Tool Development; Functional Magnetic Resonance Imaging (fMRI)‎.

  • Abstract

    This article describes the development of an interactive, web-based analytical tool to aid medical research at the medical center by allowing ‎extensive comparisons of participants with multiple variables, particularly from functional magnetic resonance imaging (fMRI) data. The ‎program performs analyses within and between people using a series of automated techniques that include brain parcellation, unsupervised ‎clustering, and data visualisation. Ward's hierarchical clustering method divided fMRI signals into functionally coherent regions, and participants were classified using K-means clustering based on their brain activity patterns. Principal Component Analysis (PCA) reduced dimensionality, allowing for interactive visualisation of subject groups. The platform, built as a web application with Papaya.js, enables intuitive ‎browsing of patient information and brain activity, assisting healthcare providers in managing clinical data and generating new insights. This ‎instrument contributes to ongoing research while also laying the groundwork for future applications such as dynamic variable weighting, ‎feature significance analysis, and expansion into other data sectors‎.

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  • How to Cite

    Manivannan, R. . ., Sreelatha, G. ., Pragathi, Y. V. S. S. ., & Nagamani, P. . . (2025). Multidimensional Data Analysis of The FMRI Attention Paradigm ‎Using Machine Learning and Web Tool Development for Interactive Knowledge Discovery. International Journal of Basic and Applied Sciences, 14(2), 484-492. https://doi.org/10.14419/7v3bz393