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

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.