Revolutionizing Healthcare Analytics with A Robust Model for ‎Secure Data Management and Superior Disease Prediction - Senthamarai, S.; Mala , Dr. R.; Palanisamy , Dr. V. (International Journal of Basic and Applied Sciences, 2025)
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.