Isolation Forest Anomaly Detection in Vital Sign Monitoring for Healthcare

The use of the isolate forest technique for recognizing anomalies in monitoring vital signs in healthcare is examined in this work. A deductive approach, based on interpretivism, uses secondary data along with a descriptive design. The procedure's strong metrics of performance are demonstrated by the results, wherein effective identification of anomalies is indicated by high precision, recollection, and AUC numbers. Its advantage over conventional methods is demonstrated by comparisons. The impact of parameter tuning is discussed, highlighting the careful balancing act between mathematical efficiency and accuracy. Opportunities healthcare issues can be qualitatively understood through the assessment of anomalies that have been detected. Improvements to interpretability, validation of results with medical professionals, and parameter refinement are among the suggestions put forward. Parameter improvement, understanding, real-world verification, and combined models should be the main areas of future research.