Personalized Assistance with Support Vector Machines and Iot for Smart Operating Rooms - J. Mahil, J. Mahil; Jones, C. Berin; Paul, Vince; M. Sucharitha, M. Sucharitha; Yamini, K. Anish Pon; Raja, R. Arshath; N. Yuvaraj, N. Yuvaraj (Journal of Neonatal Surgery, 2025)
Advancements in artificial intelligence (AI) and the Internet of Things (IoT) have significantly improved the precision and efficiency of modern surgical procedures. Smart operating rooms integrate real-time data from IoT-enabled surgical instruments and patient monitoring systems, enhancing decision-making and reducing surgical errors. However, current AI-based surgical assistance systems face challenges in terms of real-time responsiveness and adaptability to complex surgical environments. Combining Support Vector Machines (SVM) with IoT enables real-time data processing and predictive analysis, offering enhanced surgical precision and safety. The proposed system integrates IoT-based surgical instruments and monitoring devices to collect real-time data such as heart rate, blood pressure, oxygen levels, and instrument position. The SVM algorithm processes this data to predict potential complications, optimize surgical trajectories, and provide real-time feedback to the surgeon. A feedback loop between the IoT devices and the SVM model allows adaptive learning and continuous improvement during the procedure. Experimental results show that the proposed method achieves a 94.6% accuracy in surgical complication prediction, outperforming existing Convolutional Neural Network (CNN) and Decision Tree (DT) models, which achieved 91.2% and 88.5% accuracy, respectively. The proposed system enhances surgical safety and efficiency by providing real-time decision support and automated responses, leading to a 15% reduction in procedural errors and a 20% improvement in surgical completion time.