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Predicting Mortality Rate based on Comprehensive Features of Intensive Care Unit Patients
Conference proceeding   Peer reviewed

Predicting Mortality Rate based on Comprehensive Features of Intensive Care Unit Patients

Jagan Moahan Reddy Danda, Kumar Priyansh, Hossain Shahriar, Hisham Haddad, Alfredo Cuzzocrea and Nazmus Sakib
2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp.1402-1407
Annual Computers, Software, and Applications Conference (COMPSAC), 46th (Los Alamitos, California, USA, 06/27/2022–07/01/2022)
08/2022
DOI: https://doi.org/10.1109/COMPSAC54236.2022.00222
Web of Science ID: WOS:000855983300214

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Abstract

Classification Intensive Care Unit MIMIC-IV Mortality rate Predictive analytics Death or Mortality Health Care Machine Learning
Predictive analytics is gaining momentum in health-care since the adoption of electronic health record (EHR) system in hospitals. In particular, machine learning models are built using the critical care EHR data and the information provided during the ICU admissions to predict the mortality of patients admitted in ICU. As per the MIMIC-IV dataset, the survival rate of patients admitted in ICU is found to be 89.76%. This paper proposes a hybrid prediction technique that uses Random Forest and XGBoost for predicting the mortality rate. The proposed techniques performed well in predicting mortality rate despite the class imbalance problem of the dataset. The experiments conducted on MIMIC-IV dataset yields prediction accuracy of 89.72%.

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