Comput. Biol. Med. - Artificial neural network modelling of the results of tympanoplasty in chronic suppurative otitis media patients.

Tópicos

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Resumo

The application of computer modelling for medical purposes, although challenging, is a promising pathway for further development in the medical sciences. We present predictive neural and k-nearest neighbour (k-NN) models for hearing improvements after middle ear surgery for chronic otitis media. The studied data set comprised 150 patients characterised by the set of input variables: age, gender, preoperative audiometric results, ear pathology and details of the surgical procedure. The predicted (output) variable was the postoperative hearing threshold. The best neural models developed in this study achieved 84% correct predictions for the test data set while the k-NN model produced only 75.8% correct predictions.

Resumo Limpo

applic comput model medic purpos although challeng promis pathway develop medic scienc present predict neural knearest neighbour knn model hear improv middl ear surgeri chronic otiti media studi data set compris patient characteris set input variabl age gender preoper audiometr result ear patholog detail surgic procedur predict output variabl postop hear threshold best neural model develop studi achiev correct predict test data set knn model produc correct predict

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