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Mortality Rate Prediction in ICU Using Logistic Regression Method

K V Sruthi1 , K Manju2 , R K Rashmi3 , R Krishnamouli4

  1. Computer Science Department, MVJ College of Engineering, VTU University,Bangalore-67, India.
  2. Computer Science Department, MVJ College of Engineering, VTU University,Bangalore-67, India.
  3. Computer Science Department, MVJ College of Engineering, VTU University,Bangalore-67, India.
  4. Department of Big Data Analytics, St.Joesph’s College, Autonomous, Bangalore, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 668-674, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.668674

Online published on May 31, 2018

Copyright © K V Sruthi, K Manju, R K Rashmi, R Krishnamouli . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: K V Sruthi, K Manju, R K Rashmi, R Krishnamouli, “Mortality Rate Prediction in ICU Using Logistic Regression Method,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.668-674, 2018.

MLA Style Citation: K V Sruthi, K Manju, R K Rashmi, R Krishnamouli "Mortality Rate Prediction in ICU Using Logistic Regression Method." International Journal of Computer Sciences and Engineering 6.5 (2018): 668-674.

APA Style Citation: K V Sruthi, K Manju, R K Rashmi, R Krishnamouli, (2018). Mortality Rate Prediction in ICU Using Logistic Regression Method. International Journal of Computer Sciences and Engineering, 6(5), 668-674.

BibTex Style Citation:
@article{Sruthi_2018,
author = {K V Sruthi, K Manju, R K Rashmi, R Krishnamouli},
title = {Mortality Rate Prediction in ICU Using Logistic Regression Method},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {668-674},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2040},
doi = {https://doi.org/10.26438/ijcse/v6i5.668674}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.668674}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2040
TI - Mortality Rate Prediction in ICU Using Logistic Regression Method
T2 - International Journal of Computer Sciences and Engineering
AU - K V Sruthi, K Manju, R K Rashmi, R Krishnamouli
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 668-674
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

High risk of illness is observed for the patients admitted in hospital’s cardiac intensive care units (ICU). Patient’s dead/alive categorical outcome prediction would benefits for patients as well as medical professionals in creating awareness and making clinical decisions respectively. In this work, a model is proposed for predicting life outcomes of cardiac patients admitted in ICU. The model is prepared on the basis of data collected from the regular medication treatments and clinical laboratory test results. A logistic regression model is prepared and compared with two standard algorithms in machine learning such as artificial neural network (ANN) and random forest algorithms, which are the classifiers of decision tree. The performance parameters were compared for both Synthetic Minority Oversampling Technique and stratified sampling for all predictive learning models. It is concluded that logistic regression with stratified sampling techniques would be preferable as a predictive model for the inconsistent time series data set.

Key-Words / Index Term

Predictive learning, logistic regression, SMOTE sampling, stratified sampling, time series data

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