SwePub - sökning: WFRF:(Tafazzoli A)

Numrering	Referens	Omslagsbild	Hitta
1.	Handels, RLH, et al. (författare) Cost-effectiveness models for Alzheimer's disease and related dementias: IPECAD modeling workshop cross-comparison challenge 2023 Ingår i: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279 .- 1552-5260. ; 19:5, s. 1800-1820 Tidskriftsartikel (refereegranskat)
2.	Pezeshki, Zahra, et al. (författare) Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran 2016 Ingår i: International Journal of Enteric Pathogens. - : Maad Rayan Publishing Company. - 2345-3362 .- 2322-5866. ; 4:1, s. 23-30 Tidskriftsartikel (refereegranskat)abstract Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera.Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model.Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters.Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other.Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

Pezeshki, Zahra, et al. (författare)
Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran
2016
Ingår i: International Journal of Enteric Pathogens. - : Maad Rayan Publishing Company. - 2345-3362 .- 2322-5866. ; 4:1, s. 23-30
Tidskriftsartikel (refereegranskat)abstract
- Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera.Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model.Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters.Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other.Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

Träfflista för sökning "WFRF:(Tafazzoli A) "

Avgränsa träffmängd

År