Neural network diagnostic system for dengue patients risk classification

Faisal, Tarig; Taib, Mohd Nasir; Ibrahim, Fatimah

doi:10.1007/s10916-010-9532-x

Cited by 18 publications

(14 citation statements)

References 22 publications

(32 reference statements)

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“…Thus, this work and recent findings [7,8] have shown that the noninvasive intelligent system and neural network are promising techniques to predict risk in dengue patients. However, these results of single bioimpedance measurements is limited to the extracellular cell and can be enhanced and improved by using bioimpedance spectroscopy techniques [23,24,33] where measurement can be obtained in the intracellular cell at different frequencies ranges.…”

Section: Discussionmentioning

confidence: 62%

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

Ibrahim

Faisal

Salim

et al. 2010

Med Biol Eng Comput

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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%.

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Section: Discussionmentioning

confidence: 62%

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

Ibrahim

Faisal

Salim

et al. 2010

Med Biol Eng Comput

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“…20 ANN is an important tool for data mining of medical records for classification and prediction purposes. 22 In a large number of previous studies, neural network was used for classifying such diseases as dengue fever, [23][24][25] chest or heart diseases, 26,27 West Nile virus diseases, 28 tuberculosis, 29,30 gestational diabetes mellitus, 31 swine flu, 32 and pancreatic cancer. 33 These studies had helped in diagnosis and case management of epidemic victims.…”

Section: The Applications Of Ann In Epidemiologymentioning

confidence: 99%

A Review of Epidemic Forecasting Using Artificial Neural Networks

Philemon

Ismail

Jayeola

2019

Int J Epidemiol Res

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Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

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“…The aims of our work are to 1) present recurrent neural networks for time ahead predictive modelling as a highly flexible tool for outbreak prediction, and 2) implement and evaluate the model performance for the Zika epidemic in the Americas. The application of neural networks for epidemic risk forecasting has previously been applied to dengue forecasting and risk classification [45][46][47][48][49][50], detection of mosquito presence [51], temporal modeling of the oviposition of Aedes aegypti mosquito [52], Aedes larva identification [53], and epidemiologic time-series modeling through fusion of neural networks, fuzzy systems and genetic algorithms [54]. Recently, Jian et al [55] performed a comparison of different machine learning models to map the probability of Zika epidemic outbreak using publically available global Zika case data and other known covariates of transmission risk.…”

Section: Introductionmentioning

confidence: 99%

A dynamic neural network model for predicting risk of Zika in real-time

Akhtar

Kraemer

Gardner

2018

Preprint

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LG) 1617 Americas with the overall average accuracy remaining above 85% even for prediction 42 windows of up to 12 weeks. 43 44 Conclusions 45Sensitivity analysis illustrated the model performance to be robust across a range of 46 features. Critically, the model performed consistently well at various stages 47 throughout the course of the outbreak, indicating its potential value at any time during 48 an epidemic. The predictive capability was superior for shorter forecast windows, and 49 geographically isolated locations that are predominantly connected via air travel. 50The highly flexible nature of the proposed modeling framework enables policy 51 makers to develop and plan vector control programs and case surveillance strategies 52 which can be tailored to a range of objectives and resource constraints. 53 54 Keywords 55 Zika, epidemic risk prediction, dynamic neural network 56 57 58 59 60 4 Background 61 The Zika virus, which is primarily transmitted through the bite of infected Aedes 62 aegypti mosquitoes (1), was first discovered in Uganda in 1947 (2) from where it 63 spread to Asia in 1960s, where it since has caused small outbreaks. In 2007 ZIKV 64 caused an island wide outbreak in Yap Island, Micronesia (3), followed by outbreaks 65 in French Polynesia (4) and other Pacific islands between 2013 ̶ 2014 where attack 66 rates where up to 70% (5-7). It reached Latin America between late 2013 and early 67 2014, but was not detected by public health authorities until May 2015 (8) and since 68 affected 48 countries and territories in the Americas (9-11). Since there is no 69 vaccination or treatment available for Zika infections (12, 13), the control of Ae. 70 aegypti mosquito populations remains the most important intervention to contain the 71 spread of the virus (14). In order to optimally allocate resources to suppress vector 72 populations, it is critical to accurately anticipate the occurrence and arrival time of 73 arboviral infections to detect local transmission (15). 74 75 Whereas for dengue, the most common arbovirus infection, prediction has attracted 76 wide attention from researchers employing statistical modelling and machine learning 77 methods to guide vector control (16-29), such real-time machine learning based 78 models do not yet exist for Zika virus. Early warning systems for Thailand, Indonesia, 79 Ecuador and Pakistan have been introduced and are currently in use (30-34). In 80 addition to conventional predictions based on epidemiological and meteorological 81 data (20, 35, 36), more recent models have successfully incorporated search engines 82 (37, 38), land use (39), human mobility information (40, 41) and spatial dynamics 83 (42-44), and various combinations of the above (45) to improve predictions. Whereas 84 local spread may be mediated by overland travel, continent wide spread is mostly 85 driven by air passenger travel between climatically synchronous regions (46-52). 86 87 The aims of our work are to 1) present recurrent neural networks for time ahead 88 predictive modelling as ...

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Neural network diagnostic system for dengue patients risk classification

Cited by 18 publications

References 22 publications

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

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

A Review of Epidemic Forecasting Using Artificial Neural Networks

A dynamic neural network model for predicting risk of Zika in real-time

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