ICU Patient Risk Level Monitoring System using Supervised Learning Approaches

ICU Patient Risk Level Monitoring System using Supervised Learning Approaches M Veeresh Babu , R Harshitha , A Keerthana , K Haripriya , N Manisha 1 4 (October-December) Presentday intensive care units or ICUs offer critically ill patients who are susceptible to several problems including death and morbidity roundtheclock monitoring ICU environments produce a lot of data and call for a high stafftopatient ratio Making decisions and interpreting data in real time is a difficult task for clinicians In intensive care units ICUs machine learning ML tools are advancing the early detection of highrisk events because of more powerful computers and publicly accessible databases like the Medical Information Mart for Intensive Care MIMIC Techniques in ICU settings uses MIMIC data The ICU patient risk level monitoring system plays a crucial role in improving patient safety optimizing resource allocation and enhancing clinical decisionmaking in intensive care settings By continuously monitoring and analyzing patient data it provides valuable insights that help healthcare providers intervene promptly and prevent adverse outcomes

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