Real-Time Health Data Analytics: Integrating IoT and Machine Learning for Early Detection of Chronic Diseases - Sudhakar, Kattupalli; Kishore, Sajja Kirishna; Krishna, J.S.V. Gopala; N. Sangma, Tangme Marchina; Kumar, Bathula Prasanna (Journal of Neonatal Surgery, 2025)
The integration of Internet of Things (IoT) and machine learning technologies in healthcare has the potential to revolutionize the early detection and management of chronic diseases. This paper presents a comprehensive architecture for real-time health data analytics, leveraging IoT devices and advanced machine learning algorithms to monitor, analyze, and predict health conditions. IoT Devices and Sensors: Wearable devices and environmental sensors continuously collect diverse health metrics, including heart rate, blood pressure, glucose levels, and air quality. These data streams are aggregated through IoT gateways and transmitted via message brokers like Apache Kafka. Data Processing Layer: Real-time data processing frameworks such as Apache Spark Streaming and Apache Flink are employed to handle the high-throughput data. Processed data is stored in scalable databases like Apache Cassandra, ensuring efficient data management and retrieval. Machine Learning Layer: Historical health data is utilized to train machine learning models using frameworks like TensorFlow and PyTorch. These models are deployed for real-time inference, enabling the detection of anomalies and early warning signs of chronic diseases. Analytics and Visualization: Advanced visualization tools like Grafana and Kibana provide healthcare providers with actionable insights through intuitive dashboards. Alerting mechanisms are implemented to notify providers of critical health events, facilitating timely interventions. Security and Compliance: The architecture ensures robust data security through encryption and strict access control policies. Compliance with healthcare regulations such as HIPAA and GDPR is maintained to protect patient privacy and data integrity. Feedback Loop: Continuous monitoring and user feedback are integral to the system, allowing for ongoing improvements and updates to the machine learning models and overall system performance. Case Studies and Statistics: The paper includes case studies demonstrating the effectiveness of the proposed architecture in reducing hospital readmission rates and improving patient outcomes. Statistical analyses highlight the accuracy and reliability of the machine learning models in predicting chronic disease onset