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 language | English |
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Title of host publication | 2021 9th International Conference on Information and Communication Technology (ICoICT) |
Place of Publication | New York, NY |
Publisher | IEEE |
Pages | 25-30 |
Number of pages | 6 |
ISBN (Electronic) | 9781665404471 |
ISBN (Print) | 9781665447102 |
DOIs | |
Publication status | Published - 6 Sept 2021 |
Event | 9th International Conference on Information and Communication Technology - Yogyakarta, Indonesia Duration: 3 Aug 2021 → 5 Aug 2021 |
Conference
Conference | 9th International Conference on Information and Communication Technology |
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Abbreviated title | ICoICT 2021 |
Country/Territory | Indonesia |
City | Yogyakarta |
Period | 3/08/21 → 5/08/21 |
Keywords
- COVID-19
- hypertension
- radio frequency
- pandemics
- pulmonary diseases
- nose
- artificial neural networks