ELIZA

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300943 occurrences (No.75 in the rank) during 5 years in the PubMed. [no cache] 500 found

312) In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models.

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PMID:24304230 DOI:10.1080/17457300.2013.857695

2015 International journal of injury control and safety promotion

* Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

- Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

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(2)85 the	(7)4 which	(12)2 24	(17)2 in
(3)22 a	(8)3 DNA	(13)2 an	(18)2 serum
(4)11 and	(9)3 therefore,	(14)2 effects	(19)2 six
(5)8 it	(10)3 three	(15)2 further

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