A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients
Document Type
Article
Publication Date
6-1-2024
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
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
Divisions
anaesthesiology
Publication Title
Healthcare Analytics
Volume
5
Publisher
Elsevier Inc.
Additional Information
All Open Access, Gold Open Access