IoT and AI-Driven Predictive Healthcare Systems: The Intersection of Machine Learning, Blockchain, and Healthcare Management Practices - Akbar, Shaik; Seerapu, Venkata N; D.C, Kiruthikka.; M. Sivachitra, M. Sivachitra; Kumar Shanmugam, Sathish; Reddy Penubaka, Kiran Kumar (Journal of Neonatal Surgery, 2025)
Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technologies are converging to shape predictive healthcare systems that are intelligent, secure and real time patient care. The main focus of this research is the integration of these technologies for enhanced predictive diagnostics, health monitoring and data management. Four machine learning algorithms i.e. Random Forest, Long Short Term Memory (LSTM), Support Vector Machine (SVM) and XGBoost are implemented and evaluated using real world healthcare datasets. Metrics such as accident prediction, accident Localiziation, accident Explanations and Dynamic Route Choice were measured as the performance of these models and as such, it was found out that Lstm had the highest accuracy of 96.8, XgBoost had achieved with 94.5, Random Forest with 92.3 and Support Vector Machine with 90.7. Results show that real time IoT data used along with Blockchain supported AI algorithms make early disease prediction and health trend analysis more accurate and efficient. It also increases data transparency and security, solving important issues of healthcare information systems. Further comparative analysis with related work confirms further that the proposed model has good precision, recall and data integrity. In this study, this framework demonstrates the practicality of techno-medical application for next generation of healthcare system, providing more proactive and personalized healthcare services.