Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks

Simon Simarmata ; Panser karo-karo ; Rino Ferdian Surakusumah ; Ahmad Budi Trisnawan ; Suyahman Suyahman ; Bentar Priyopradono
International Journal of Computer Technology and S... 📅 2024 Vol 1, No 2 🔗 DOI: 10.62951/ijcts.v1i2.380
Abstract
The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.
Keywords
Convolutional Neural Networks; Deep Learning; Healthcare Analytics; Long Short-Term Memory; Predictive Modeling
How to Cite

Simon Simarmata, et al. (2024). Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks. International Journal of Computer Technology and Science, 1(2). https://doi.org/10.62951/ijcts.v1i2.380

Simon Simarmata; Panser karo-karo; Rino Ferdian Surakusumah; Ahmad Budi Trisnawan; Suyahman Suyahman; Bentar Priyopradono, "Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks," International Journal of Computer Technology and Science, vol. 1, no. 2, 2024.

Simon Simarmata; Panser karo-karo; Rino Ferdian Surakusumah; Ahmad Budi Trisnawan; Suyahman Suyahman; Bentar Priyopradono. "Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks." International Journal of Computer Technology and Science, vol. 1, no. 2, 2024.

Simon Simarmata; Panser karo-karo; Rino Ferdian Surakusumah; Ahmad Budi Trisnawan; Suyahman Suyahman; Bentar Priyopradono. "Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks." International Journal of Computer Technology and Science 1, no. 2 (2024).

Simon Simarmata, et al. (2024) 'Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks', International Journal of Computer Technology and Science, 1(2). doi: 10.62951/ijcts.v1i2.380.

Simon Simarmata; Panser karo-karo; Rino Ferdian Surakusumah; Ahmad Budi Trisnawan; Suyahman Suyahman; Bentar Priyopradono. Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks. International Journal of Computer Technology and Science. 2024;1(2).

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