Revolutionizing Healthcare Analytics with A Robust Model for ‎Secure Data Management and Superior Disease Prediction

  • Authors

    • S. Senthamarai Research Scholar, Department of Computer Applications, Alagappa University, Karaikudi, Tamilnadu, India
    • Dr. R. Mala Asst. Prof & Head, Department of Computer Science, Government Arts and Science College for Women, Paramakudi, Tamilnadu, India
    • Dr. V. Palanisamy Senior Professor & Head, Department of Computer Applications, Alagappa University, Karaikudi, Tamilnadu. India
    https://doi.org/10.14419/mr62y398

    Received date: May 2, 2025

    Accepted date: May 22, 2025

    Published date: July 8, 2025

  • Healthcare data, Security; Robust Scale; Blowfish-ECDH with HMAC; Isomap; Greedy Forward Feature Selection; Particle Swarm-optimized ‎Accentuate Attentive Layer Convo Recurrence Network (PS-AAL-CRNN)‎.

  • Abstract

    In healthcare, ensuring secure patient data management and leveraging predictive analysis are pivotal for enhancing medical diagnostics and ‎treatment. The exponential growth in healthcare data, while fostering innovative solutions, raises concerns about data security and effective ‎disease prediction. Traditional security approaches often fall short against sophisticated cyber threats, risking patient privacy. This research ‎addresses these challenges comprehensively, proposing a model Particle Swarm-optimized Accentuate Attentive Layer Convo Recurrence ‎Network (PS-AAL-CRNN) to safeguard patient data and advance disease prediction through sophisticated techniques. The research utilizes ‎a healthcare dataset as the foundation for analysis and prediction. Preprocessing, this involves label encoding for categorical variables and ‎robust scaling to mitigate the impact of outliers. Introduce data security by using hybrid encryption scheme, employing Blowfish-Elliptic ‎Curve Diffie- Hellman (ECDH) for secure key exchange and Hash-Based Message Authentication Code (HMAC) for data integrity ‎verification. Using Isomaptechnique to extracting essential features through nonlinear dimensionality reduction. For feature selection employ ‎the Greedy Forward Feature Selection (GFFS) to optimize disease prediction by selectively identifying and retaining highly relevant ‎features.Classification is performed using a PS-AAL-CRNN, with an attentive layer emphasizing critical features for precise disease ‎prediction.Our model achieved better accuracy of 97.09%, precision of 97.97%, recall of 94.17%, f1-score of 96.03%, R2 of 0.843, PRC of ‎‎0.9883 with existing methods in performance evaluation‎.

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  • How to Cite

    Senthamarai, S., Mala , D. R. ., & Palanisamy , D. V. . (2025). Revolutionizing Healthcare Analytics with A Robust Model for ‎Secure Data Management and Superior Disease Prediction. International Journal of Basic and Applied Sciences, 14(SI-1), 122-130. https://doi.org/10.14419/mr62y398