PREDICTING MORTALITY RATE IN ICU USING MACHINE LEARNING: A STUDY

Abstract

Mortality rate is the measure of number of death in a limited population or by a particular cause within a certain time period. In healthcare system Intensive Care unit (ICU) plays an important role for critical condition patients. Mortality prediction of critical condition ICU patients who needs special care is a major problem of concern. The focus of this work is to predict ICU patient’s mortality by the use of health record from ICU. Nowadays, machine learning plays an important role to resolve many health related issues which includes handling of patient’s health related data and records, development of new medical procedures and the treatment of disease like cancer, heart disease, stroke, diabetes and arthritis etc. Various machine learning models are used to analyze health records to come up with solutions for different health related issues. In this work, four popular supervised machine learning algorithms, Decision Tree(DT), Random Forest (RF), K-Nearest Neighbors (KNN) and Logistic Regression(LR) has been used to predict patients mortality in ICU. In this work, In Hospital Mortality Prediction dataset which is part of MIMIC-III database has been used. The dataset is available to download and free to use from Kaggle. In our work of mortality prediction, a maximum accuracy of 0.87 has been achieved.