Bio-Inspired Approach for Estimation of Parkinson’s Disease Using Augmented Feature Selection Model

  • Authors

    • Sasi G Professor,Department of Electronics and Communication Engineering, Chettinad Institute of Technology, Chettinad Academy of Research and Education, Manamai off Campus,Chengalpattu 603 102
    • K. Selvakumarasamy Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,SIMATS 602 105
    • P.Chandra Sekar Professor and Head, Department of AI and DS Dhanalakshmi Srinivasan College of Engineering Coimbatore, Tamil Nadu, India
    • G.P. Bharathi Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS 602 105
    • V. Elamaran Department of Electronics and Communication Engineering, School of EEE, SASTRA Deemed University, Thanjavur, India
    • Murugananth Gopalraj Professor, Department of Electrical and Electronics Engineering, Ahalia School of Engineering and Technology, Palakkad, Kerala
    • V. Krishnamoorthy Professor, Dean R&D, Department of ECE, St.Michael College of Engineering and Technology Kalayarkoil, Sivagangai District, India
    https://doi.org/10.14419/dh575a48

    Received date: July 31, 2025

    Accepted date: September 11, 2025

    Published date: September 16, 2025

  • PD progression, Bio-Inspiration Model, tele monitoring dataset, L2 norm penalty

  • Abstract

    The quality of life for millions of people worldwide is impacted by Parkinson's disease (PD), one of the most prevalent neurological diseases. The Unified Parkinson's Disease Rating Scale (UPDRS) is widely utilized to evaluate this degenerative neurological condition that impairs brain function. PD is a degenerative, long-term neurological condition that impairs movement. Medication can be started earlier with the aid of early detection. It can help the patient sustain a high quality of life for a longer period by considerably slowing down the disease's course. Researchers have recently underlined the need to look at different parts of the human brain to examine changes occurring in brain tissue and to gain a more thorough understanding of PD. Therefore, it is necessary to do precise diagnostics and treatment planning to detect the disease early. The submitted work proposes an augmented feature selection and estimation (AFSE) with a network model to forecast the course of PD utilizing the monitoring dataset's condensed input feature space. The new optimized architecture with a norm penalty (L2) tuned parameter receives the reduced input feature space and then uses it to analyze the prediction performance in PD progression. Several experiments are conducted to assess the performance, and the outcomes are compared to those of previously developed algorithms on the same dataset.

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  • How to Cite

    G, S., Selvakumarasamy , K. ., Sekar , P. ., Bharathi , G., Elamaran , V., Gopalraj , M. ., & Krishnamoorthy , V. . (2025). Bio-Inspired Approach for Estimation of Parkinson’s Disease Using Augmented Feature Selection Model. International Journal of Basic and Applied Sciences, 14(5), 535-548. https://doi.org/10.14419/dh575a48