Enhancing healthcare classification with hybrid multimedia data processing and deep learning TNBO FCNN approach in...

The rapid expansion of IoT-based healthcare systems has led to an explosion of multimodal patient data (e.g., voice, images, signals), raising concerns about accurate classification, real-time processing, and data security. Existing machine learning approaches often suffer from limited feature extraction, high computational demands, and inadequate privacy protection, particularly in decentralised and resource-constrained environments. Furthermore, the lack of robust routing and data integrity mechanisms hinders their deployment in real-world healthcare applications. To address these challenges, we propose a hybrid deep learning framework that integrates Tunable Nonlinear Bayesian Optimisation (TNBO) for efficient routing and hyper-parameter tuning with a Fully Connected Neural Network (FCNN) for robust classification of multimodal healthcare data. Additionally, we embed blockchain technology to ensure the security, transparency, and immutability of patient data across decentralised IoT environments. Our method demonstrates superior performance in terms of accuracy, sensitivity, and specificity when compared to traditional models such as RTS-DELM and SCA_WKNN. We optimise the proposed architecture for energy efficiency, scalability, and real-time healthcare monitoring, positioning it for deployment in telemedicine and smart healthcare systems. Experimental validation on benchmark datasets confirms the efficacy of our model, while ethical implications and future research directions are also discussed to support broader applicability. The performance of the proposed model was evaluated against existing state-of-the-art techniques, including Hybrid Multimedia Data Processing, RTS-DELM, Multimedia Data Sharing Systems, and SCA_WKNN. The results indicated that the TNBO + FCNN model achieved an accuracy of 0.924, a sensitivity of 0.921, and a specificity of 0.926. Furthermore, the model outperformed comparative approaches by significant margins, achieving an F1-score of 0.915 and an area under the receiver operating characteristic curve (AUC-ROC) of 0.950. These findings validate the proposed model's effectiveness in enhancing healthcare data classification, making it a promising solution for real-time monitoring and decision-making in healthcare systems.