Neural networks for predicting antibiotic resistance - an analysis of performance
Examensarbete för masterexamen
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|Type: ||Examensarbete för masterexamen|
|Title: ||Neural networks for predicting antibiotic resistance - an analysis of performance|
|Authors: ||Skyman, Beatrice|
|Abstract: ||This thesis investigate the possibility of predicting bacteria resistant to antibiotic
treatment. The investigation is a part of developing a diagnostic tool for hospitals
to use when deciding which antibiotic a patient should be treated with. This tool
will be more and more relevant as antibiotic resistance and multidrug-resistance is
spreading in the world with an accelerating rate.
Neural networks are used for predicting the probability of a bacterium being resistant
to an antibiotic. The architecture of these networks is investigated and their
outcome and performance are analysed. The distribution of the predictions are investigated
and then different classification limits are tested. A classification limit
gives a number of unclassified samples and errors, and these samples are investigated
with regards to age, gender and country. Finally, the performance of the model is
measured as the number of tested antibiotics is reduced.
When investigating the architecture of the neural networks the result are quite
similar regardless to the number of layers and neurons. For the predictions, some
antibiotics are more easily separated than other. This leads to a big variation in
the number of unclassified samples and error between different antibiotics. When
analysing the unclassified samples and errors only country could affect the predictions.
The performance of the model when having more than four antibiotics tested
as input is high. The conclusion is therefore that predicting antibiotic resistance
using neural networks is possible and could potentially be used to replace measurements
in the hospital laboratory.|
|Keywords: ||antibiotic resistance, neural networks, machine learning, predicting performance, diagnostic|
|Issue Date: ||2020|
|Publisher: ||Chalmers tekniska högskola / Institutionen för matematiska vetenskaper|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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