Prognostic Stacking Machine Learning Model for Intensive Care Unit Admission Prediction of COVID Patients

Richa Sharma, Himanshu Pandey, Ambuj Kumar Agarwal, Dolley Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Statistical methods and models are the backbone of machine learning, which allows computers to autonomously discover and exploit data patterns. The ultimate objective is for computers to accurately forecast outcomes based on historical data. In this work, we present two related research projects: one on stacking and the other on boosting, two ensemble learning approaches. This research investigates its behavior from several angles and sheds light on its relationship to related ensemble learning schemes by showing that it is typically the best option and that stacking can be used to replicate the vast majority of ensemble learning schemes. The COVID-19 clinical dataset has been used to test these strategies for use in anticipating and preparing healthcare systems to avert collapse, where collapse is defined as an over-capacity demand of ICU beds. F1-measures stacking is superior to other methods in terms of accuracy, precision, and recall, as shown by the findings.

Original languageEnglish
Title of host publicationProceedings of 4th Doctoral Symposium on Computational Intelligence - DoSCI 2023
EditorsAbhishek Swaroop, Vineet Kansal, Giancarlo Fortino, Aboul Ella Hassanien
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9789819937158
StatePublished - 1 Jan 2023
Externally publishedYes
Event4th Doctoral Symposium on Computational Intelligence, DoSCI 2023 - Virtual, Online
Duration: 3 Mar 20233 Mar 2023

Publication series

NameLecture Notes in Networks and Systems
Volume726 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference4th Doctoral Symposium on Computational Intelligence, DoSCI 2023
CityVirtual, Online


  • AI applications
  • Artificial intelligence
  • COVID-19
  • Deep learning
  • Machine learning
  • Pandemic

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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