Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

Abstract

This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm(3)), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network's performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.

Mathematical modelling and a systems science approach to describe the role of cytokines in the evolution of severe dengue

“…ANFIS uses properties of ANN in developing fuzzy membership functions. An ANN approach in [48] classified the risk of dengue patients with an accuracy of 96.27%. To use ANN and ANFIS techniques it requires a larger data set as the data set has to be divided as training data set and testing data set and the model is trained using this sample training data set.…”

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

“…In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined. The current accuracy for prediction systems based on climate factors ranges from 82.39% to 90.5% [16,[20][21][22][23][24][25].…”

“…Hybrid models have been used in outbreak prediction research. A hybrid model is an example of an integrated model, and many models based on genetic algorithms are available to determine the weight in a neural network model [14,17,18,19,25,26]. In Singapore, researchers found signi cant correlated dengue cases with climatic variables by using a Poisson regression model [27].…”

“…Vulnerability maps of dengue incidences have been generated in Malaysia, resulting in the development and implementation of visualised and predictive modelling using geographic information systems (GIS) for dengue fever in Selangor, Malaysia [28]. There are different models of dengue outbreak prediction systems in Malaysia have achieved different accuracies [16,25].In 2015, [29] predicted localised dengue incidences in Malaysia using an ensemble system for identi cation and found that ensemble models exhibit better prediction power than a single model [29].The prediction of dengue outbreaks is crucial worldwide because this infectious disease remains as a major issue in many countries [14,26,30,31]. Table 1 lists studies on different models of dengue outbreak prediction with distinct climatic risk factors.…”

Diagnosis and Prognosis of the Arbovirus-Dengue using Intelligent Algorithm

“…In [19], the authors used BIA and ANN to analyze the data of nearly 223 healthy subjects and 207 hospitalized dengue patients. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group's quantification.…”