Swarup Nandi, Madhusudhan Misra, Swanirbhar Majumder
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.
Mortality Prediction, ICU, DT,
RF, KNN, LR, Confusion Matrix.
VOL.15, ISSUE No.1, March 2023