Multidimensional Data Analysis of The FMRI Attention Paradigm ‎Using Machine Learning and Web Tool Development for Interactive Knowledge Discovery - Manivannan, R .; Sreelatha, Gavini; Pragathi, Y V S Sai; Nagamani, P . (International Journal of Basic and Applied Sciences, 2025)
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.