Personalized Assistance with Support Vector Machines and Iot for Smart Operating Rooms
DOI:
https://doi.org/10.52783/jns.v14.3987Keywords:
Surgical Assistance, Support Vector Machine, IoT, Smart Operating Rooms, Real-Time PredictionAbstract
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.
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