IoT and Random Forest-based Solutions for Blood Gas Quality Assurance in Clinical Laboratories

Integrating Internet of Things (IoT) technologies and Random Forest algorithms in clinical laboratories may improve blood gas analysis quality. Patients' respiratory and metabolic state may be determined through blood gas analysis. Analyses must be accurate and precise for patient care. Manual and periodic examinations in clinical labs might be time-consuming and cause problems. IoT and Random Forest-based technologies handle blood gas analysis quality assurance issues in this paper. Diagnostic lab equipment like blood gas analyzers uses IoT sensors and devices to monitor and gather data. This data is put into a Random Forest algorithm, which provides intelligent quality control. Due to its past training, the Random Forest algorithm can detect real-time deviations from predicted values, trends, or patterns. It predicts, classifies, and warns of data abnormalities using ensemble learning. Real-time monitoring and early warning may greatly minimize misdiagnosis and increase patient safety. Increased automation, less human error, enhanced data analysis, and the capacity to detect and avoid patient care difficulties are all benefits of IoT and Random Forest-based blood gas quality assurance systems. It enables easy connection with laboratory information systems, producing a complete quality control infrastructure. Improving real-time blood gas analysis systems' anomaly detection capabilities may include tracking sensors for accuracy, making data transfer more efficient, and honing machine learning models.