Chaw, Jun Kit and Chaw, Sook Hui and Quah, Chai Hoong and Sahrani, Shafrida and Ang, Mei Choo and Zhao, Yanfeng and Ting, Tin Tin (2024) A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients. Healthcare Analytics, 5. ISSN 2772-4425, DOI https://doi.org/10.1016/j.health.2023.100290.
Full text not available from this repository.Abstract
Dengue is a common viral disease in tropical and subtropical countries. The clinical manifestation of dengue has a wide spectrum, from asymptomatic seroconversion to severe dengue infection. Severe dengue is defined as dengue with the presence of specific symptoms, including severe plasma leakage leading to shock or the accumulation of fluids with respiratory distress, severe bleeding, and severe organ impairment. Examining the progression of shock with the integration of patients’ physiological information and biochemical parameters would help in understanding the progression of the disease and early detection of shock. In this study, physiological patient data diagnosed with dengue are collected from a University Malaya Medical Centre's electronic record. A prediction model learned from the measurement of a patient's physiological data is the basis for effective treatment and prevention of shock development in critically ill patients. Hence, this study presents the predictive performance of machine learning algorithms to estimate the risk of shock development among dengue patients. Logistic regression, decision trees, support vector machines and neural networks are evaluated. Lastly, ensemble learnings of bagging and boosting are also applied to the weak learner to optimize performance. The experimental results show that the bagging algorithm outperforms other competing methods with a 14.5 improvement from the individual decision tree. The full blood count (FBC) specifically haemoglobin (Hb) on day 2 is found to be a strong predictor for severe dengue occurrence. © 2023 The Authors
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | All Open Access, Gold Open Access |
| Uncontrolled Keywords: | hemoglobin; adult; Article; artificial neural network; blood cell count; clinical decision making; controlled study; critically ill patient; decision tree; dengue; disease severity; electronic medical record; female; human; machine learning; major clinical study; male; morbidity; prediction; process optimization; risk assessment; shock; support vector machine |
| Subjects: | R Medicine > RB Pathology |
| Divisions: | Faculty of Medicine > Anaesthesiology Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Jul 2024 05:28 |
| Last Modified: | 11 Jul 2024 05:28 |
| URI: | http://eprints.um.edu.my/id/eprint/44751 |
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