Early Prediction of Parkinson’s Disease by Using MathematicalModeling and Hilbert ‎Transform

  • Authors

    • Thalapathiraj S Lincoln University College, Malaysia
    • Vivekanandam B Dean, School of AI Computing and Multimedia, Lincoln University College, Malaysia
    • Rajendra Kumar Tripathi Department of Applied Sciences and Humanities (Mathematics), Faculty of Engineering and Technology, Khwaja Moinuddin Chishti Language University, Lucknow, Uttar Pradesh, India
    https://doi.org/10.14419/mx6bb868

    Received date: November 11, 2025

    Accepted date: December 19, 2025

    Published date: December 21, 2025

  • Parkinsons Disease; Hilbert Transform; Transformer Based Model; Deep Learning

  • Abstract

    This research introduces a hybrid mathematical-deep learning framework for the early ‎prediction of Parkinson’s Disease (PD) using hand-drawn spiral and wave imagery. The ‎grayscale pictures were changed using the Hilbert Transform to get amplitude and phase details ‎that show the severity of the tremor and how it moves in an irregular way. We combined these ‎attributes with the original photos and ran them through three Transformer backbones: Swin-T, ‎ViT-B/16, and BEiT-B/16. The experimental findings showed that Swin-T exhibited superior ‎performance, achieving an AUC of 0.983, sensitivity of 0.951, and accuracy of 94.1%. This ‎was followed by ViT-B/16, which attained an AUC of 0.972. In contrast, BEiT-B/16 ‎underperformed, recording an AUC of 0.613. Combining Hilbert-based mathematical modeling ‎with Transformer designs creates a strong and understandable way to do early PD screening ‎without surgery. Furthermore, the limitations resulting from the tiny clinical dataset are ‎explicitly examined, and a stability–separability formalism is given to assess the resilience and ‎discriminative strength of Hilbert-derived features‎.

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  • How to Cite

    S, T. ., B, V. ., & Tripathi, R. K. . (2025). Early Prediction of Parkinson’s Disease by Using MathematicalModeling and Hilbert ‎Transform. International Journal of Basic and Applied Sciences, 14(8), 453-460. https://doi.org/10.14419/mx6bb868