Artificial neural networks for infant mortality modelling.
This work aims to investigate a simple to use and easy to interpret methodology for assessing the relative importance of input variables in artificial neural networks (ANNs) applied to epidemiological modelling. The independent variables were 43 variables of the social, economic, environmental and health sector of 59 Brazilian municipalities, and the outcomes were infant mortality rates from these municipalities. Two assays were developed for the ANN modelling. On the first, all 43 variables were taken as input; and on the second, input variables were chosen with the help of factor analysis (FA). The relative importance of the input variables was investigated by means of bootstrap replications of the ANN model on the second assay. Further, multiple linear regression models (LRMs) were developed with the same data set and compared to the ANN models. The FA analysis allowed the selection of eight variables for the second assay. The percent of explained variance R(2) on the ANNs was in the range 0.74-0.80, while linear models had R(2)=0.4-0.5. These findings were validated by the bootstrap replications, in which the ANN models remained with higher R(2) and lower mean square error than the LRMs. The analysis of the best (second) ANN model indicated the highest ranking of importance for the variables literacy, agricultural and livestock sector jobs, number of commercial establishments and telephones. The approach presented here successfully integrated a data-oriented model with expert knowledge, indicating the potentiality of ANN modelling in the prediction, planning and assessment of public health actions.
Gismondi
Ronaldo C
RC
Medical Sciences College/State University of Rio de Janeiro, Rio de Janeiro, Brazil. renan@peb.ufrj.br
Almeida
Renan Moritz Varnier R
RM
Infantosi
Antonio Fernando C
AF
D001938:Brazil / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D016014:Linear Models; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics