Deep Learning for Disease Diagnosis in IOT Enabled Healthcare Systems: A Predictive Modelling Approach

Abstract

The integration of the Internet of Things (IoT) with deep learning has revolutionized healthcare systems, enabling real-time monitoring, predictive diagnostics, and personalized treatment. IoT-enabled devices continuously capture multimodal patient health data such as vital signs, laboratory test results, and demographic information, which, when combined with advanced machine learning and deep learning models, can significantly improve disease diagnosis. This study presents a predictive modelling framework for disease detection using IoT-driven healthcare data, focusing on both traditional machine learning algorithms (Logistic Regression, Random Forest, XGBoost) and deep learning architectures such as Long Short-Term Memory (LSTM) networks. A case study on sepsis prediction using synthetic but clinically inspired ICU datasets is conducted to demonstrate the effectiveness of the approach. Results indicate that LSTM-based fusion models achieve superior performance in terms of sensitivity, specificity, and F1-score compared to conventional classifiers, thereby reducing false alarms and improving diagnostic reliability. Furthermore, the research highlights the importance of cybersecurity and privacy-preserving methods, such as federated learning, in securing patient health data within IoT ecosystems. The findings establish that deep learning models, when integrated with IoT healthcare infrastructure, provide a robust and scalable predictive diagnostic framework capable of transforming modern clinical practices.

Introduction

Modern healthcare is shifting from reactive to proactive and predictive care, driven by the integration of Internet of Things (IoT) and deep learning (DL) technologies. IoT devices like wearables and smart monitors generate rich, real-time, multimodal patient data (e.g., vitals, labs, EHRs), which deep learning can use to enable early disease detection, personalized care, and enhanced clinical decision-making.

Challenges & Solutions

Traditional machine learning struggles with the noisy, high-dimensional, and real-time nature of IoT data. Deep learning—especially models like CNNs, LSTMs, and hybrid fusion architectures—offers superior handling of temporal patterns and multimodal inputs, leading to better diagnostic accuracy and fewer false alarms.

Literature Review Highlights

• Imaging-based DL: CNNs outperform clinicians in diabetic retinopathy and skin lesion detection, validating the use of DL in diagnosis.

• Streaming signals: DL models (e.g., LSTM, CNN) have been successful with real-time ECG and PPG data from IoT devices for arrhythmia detection.

• Sepsis prediction: Predictive models trained on EHR + real-time vitals show promise in early sepsis alerts, though implementation requires attention to clinical integration and alert design.

• Privacy & security: Federated learning (FL) allows model training across hospitals without sharing raw data, addressing privacy. However, IoT introduces cybersecurity risks like spoofing and adversarial attacks, necessitating secure transmission, authentication, and model hardening.

Key Research Themes

1. Multimodal data fusion: Combining vitals, labs, imaging, and demographics yields stronger diagnostic performance than single-modality inputs.

2. Cybersecurity-aware design: Incorporating privacy-preserving tools (FL), encryption, and secure architectures is essential for real-world deployment.

3. Model interpretability and validation: Clinical adoption requires explainable AI, well-calibrated predictions, and external validation.

Research Contributions

• A unified framework for IoT-integrated DL-based disease diagnosis.

• Comparative analysis of traditional ML models vs. deep learning models (e.g., LSTM-Fusion).

• Emphasis on cybersecurity, privacy, and real-time deployment.

• A sepsis case study using synthetic yet clinically realistic ICU data.

Methodology Overview

The proposed framework includes:

1. Problem Definition – Identify target condition (e.g., sepsis) and set predictive goals.

2. IoT Data Collection – Use wearables and smart devices to gather real-time multimodal data.

3. Preprocessing – Handle noise, missingness, and feature engineering.

4. Model Development – Train and evaluate ML (e.g., Logistic Regression, XGBoost) and DL (e.g., LSTM, CNN, hybrid fusion) models.

5. Training & Validation – Cross-validation, imbalance handling, and performance tuning.

6. Evaluation – Use metrics like Accuracy, AUROC, Sensitivity, and F1-score; generate visualizations.

7. Deployment – Real-time model integration into IoT systems with low latency and high scalability.

8. Cybersecurity Measures – Implement encryption, blockchain for integrity, and FL for privacy.

9. Monitoring & Updates – Continuously retrain and monitor model performance and interpretability (e.g., SHAP, saliency maps).

Conclusion

This study explored the integration of deep learning methodologies with IoT-enabled healthcare systems to enhance predictive disease diagnosis. By leveraging real-time multimodal patient data collected from IoT devices, the research demonstrated that deep learning models, particularly LSTM-based fusion architectures, significantly outperform conventional machine learning approaches such as Logistic Regression, Random Forest, and XGBoost. The case study on sepsis prediction highlighted how the proposed predictive framework can improve diagnostic accuracy, reduce false alarms, and support timely clinical interventions. A key contribution of this work lies in presenting a generalized methodology that combines IoT data acquisition, preprocessing, predictive modelling, and performance evaluation. In addition, the study addressed critical issues of cybersecurity and privacy, underscoring the importance of incorporating federated learning and secure IoT frameworks to safeguard sensitive patient health data in real-world deployments. The findings confirm that IoT-driven deep learning approaches hold strong potential for transforming healthcare by enabling early disease detection, proactive patient management, and personalized care delivery. Furthermore, the framework is scalable and adaptable across multiple disease domains, making it applicable not only to sepsis but also to cardiovascular disorders, diabetes, cancer detection, and other chronic illnesses. Future research directions may include the use of explainable AI (XAI) to enhance the transparency of deep learning predictions, integration of edge computing to reduce latency in IoT healthcare systems, and deployment of blockchain-enabled security protocols for more robust data privacy. These extensions can further strengthen the clinical acceptance, scalability, and trustworthiness of IoT-enabled predictive healthcare systems.

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Copyright

Copyright © 2025 Dr. M. Divyavani, Dr. G. S. Gowri, Mrs. N. V. Kalaimani, Mrs. V. R. Nithiyapriya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.