Probabilistic Risk Assessment of COVID-19 Patients at COVID-19 Assessment Centre

Autor: Choo-Yee Ting, Helmi Zakariah, Yasmin Zulaikha Mohd Yusri
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Technology, Vol 13, Iss 6, Pp 1193-1201 (2022)
Druh dokumentu: article
ISSN: 2086-9614
2087-2100
04752945
DOI: 10.14716/ijtech.v13i6.5882
Popis: COVID-19 started impacting Malaysia in early 2020, and the cases have reached 4.4 million as of April 27, 2022, with 35507 deaths. Since then, federal and state governments have set up COVID-19 Assessment Centres (CACs) to monitor, manage and assess the risk of COVID-19-positive patients. However, a large number of patients within a day has caused the CACs to experience a shortage in medical officers and subsequently resort to overwhelming administrative work. A misassignment of a patient to either home quarantine or COVID-19 Quarantine and Treatment Center or immediate hospital admission (PKRC) could potentially increase the Brought-In-Dead (BID) cases. Therefore, this study aimed to overcome the challenges by achieving the following two main objectives: (i) to identify the optimal feature sets for adult and child patients when they require hospital admission, (ii) to construct predictive models that perform preliminary assessment of a patient, which a medical officer later confirms. In this study, the predictive models developed were Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression and Decision Tree. The datasets were obtained from one of the CACs in Malaysia and were imbalanced in nature. The empirical findings showed that Logistic Regression outperformed the rest with a slight difference. The findings suggested that while there are shared symptoms among adult and child patients, such as runny nose and cough, the child patients exhibited extra symptoms such as vomiting, lung disease, and persistent fever.COVID-19 started impacting Malaysia in early 2020, and the cases have reached 4.4 million as of April 27, 2022, with 35507 deaths. Since then, federal and state governments have set up COVID-19 Assessment Centres (CACs) to monitor, manage and assess the risk of COVID-19-positive patients. However, a large number of patients within a day has caused the CACs to experience a shortage in medical officers and subsequently resort to overwhelming administrative work. A misassignment of a patient to either home quarantine or COVID-19 Quarantine and Treatment Center or immediate hospital admission (PKRC) could potentially increase the Brought-In-Dead (BID) cases. Therefore, this study aimed to overcome the challenges by achieving the following two main objectives: (i) to identify the optimal feature sets for adult and child patients when they require hospital admission, (ii) to construct predictive models that perform preliminary assessment of a patient, which a medical officer later confirms. In this study, the predictive models developed were Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression and Decision Tree. The datasets were obtained from one of the CACs in Malaysia and were imbalanced in nature. The empirical findings showed that Logistic Regression outperformed the rest with a slight difference. The findings suggested that while there are shared symptoms among adult and child patients, such as runny nose and cough, the child patients exhibited extra symptoms such as vomiting, lung disease, and persistent fever.
Databáze: Directory of Open Access Journals
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