Contributing Clinical Attributes to COVID-19 Mortality in Jakarta: Machine Learning Study

Muhamad Erza Aminanto, Bahrul Ilmi Nasution, Andi Sulasikin, Yudhistira Nugraha, Juan Kanggrawan, Alex L. Suherman

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

Abstract

Since December 2019, we have lived in a pandemic era of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Medical records of COVID-19 patients have been reported and analyzed worldwide. The Health Agency of Jakarta, Indonesia, collected clinical symptoms, demographics, travel history, and mortality information from March 2020 up to now. Despite massive research on COVID-19 patients’ data, the significant clinical symptoms that lead to COVID-19 mortality in Jakarta have not been well described to the best of the authors’ knowledge. We extracted the COVID-19 records in Jakarta and compared them between patients who were discharged and deceased. This paper examines each clinical symptom’s importance to mortality using machine learning techniques, namely weighted Artificial Neural Network, Decision Tree, and Random Forest. We observed that Pneumonia, Shortness of Breath, Malaise, Hypertension, Fever, and Runny Nose are the top six significant clinical symptoms that lead to deaths in Jakarta. We suggest medical experts become more cautious with these symptoms. Also, in medical facilities, these symptoms can be used as prescreening before entering the facilities.
Original languageEnglish
Title of host publication2021 9th International Conference on Information and Communication Technology (ICoICT)
Place of PublicationNew York, NY
PublisherIEEE
Pages25-30
Number of pages6
ISBN (Electronic)9781665404471
ISBN (Print)9781665447102
DOIs
Publication statusPublished - 6 Sept 2021
Event9th International Conference on Information and Communication Technology - Yogyakarta, Indonesia
Duration: 3 Aug 20215 Aug 2021

Conference

Conference9th International Conference on Information and Communication Technology
Abbreviated titleICoICT 2021
Country/TerritoryIndonesia
CityYogyakarta
Period3/08/215/08/21

Keywords

  • COVID-19
  • hypertension
  • radio frequency
  • pandemics
  • pulmonary diseases
  • nose
  • artificial neural networks

Fingerprint

Dive into the research topics of 'Contributing Clinical Attributes to COVID-19 Mortality in Jakarta: Machine Learning Study'. Together they form a unique fingerprint.

Cite this