title: A case study of using artificial neural networks for classifying cause of death from verbal autopsy.
abstract: BACKGROUND
  Artificial neural networks (ANN) are gaining prominence as a method of classification in a wide range of disciplines. In this study ANN is applied to data from a verbal autopsy study as a means of classifying cause of death.
  
  
  METHODS
  A simulated ANN was trained on a subset of verbal autopsy data, and the performance was tested on the remaining data. The performance of the ANN models were compared to two other classification methods (physician review and logistic regression) which have been tested on the same verbal autopsy data.
  
  
  RESULTS
  Artificial neural network models were as accurate as or better than the other techniques in estimating the cause-specific mortality fraction (CSMF). They estimated the CSMF within 10% of true value in 8 out of 16 causes of death. Their sensitivity and specificity compared favourably with that of data-derived algorithms based on logistic regression models.
  
  
  CONCLUSIONS
  Cross-validation is crucial in preventing the over-fitting of the ANN models to the training data. Artificial neural network models are a potentially useful technique for classifying causes of death from verbal autopsies. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.
authors: London School of Hygiene and Tropical Medicine, London, UK.
authors: A
authors: A
authors: Boulle
authors: D
authors: D
authors: Chandramohan
authors: P
authors: P
authors: Weller
mesh_terms: D001344:Autopsy / Q000379:methods*; D002423:Cause of Death; D002965:Classification / Q000379:methods*; D003625:Data Collection / Q000379:methods; D005002:Ethiopia / Q000453:epidemiology; D005869:Ghana / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013636:Tanzania / Q000453:epidemiology