Using Feature Selection from XGBoost to Predict MIC Values with Neural Networks

Cory Kromer-Edwards; Mariana Castanheira; Suely Oliveira

doi:10.1109/IJCNN54540.2023.10191713

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Using Feature Selection from XGBoost to Predict MIC Values with Neural Networks

Cory Kromer-Edwards, Mariana Castanheira and Suely Oliveira

2023 International Joint Conference on Neural Networks (IJCNN), pp.1-8

06/18/2023

DOI: 10.1109/IJCNN54540.2023.10191713

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Abstract

The minimum inhibitory concentration (MIC) is the lowest concentration of an antimicrobial agent (commonly known as an antibiotic) that inhibits bacterial growth. The interpretation of an MIC value for a bacterial organism, recovered from an infection guides the treatment of that infection by informing the physician if the treatment has a chance to be successful or not. Methods to generate the MIC values depend on bacterial growth and could take 24 hours to several days, delaying the implementation of appropriate therapy and increasing rates of morbidity and mortality. In this study, XGBoost and Neural Network (NN) models were used to predict the MIC values for 13 antimicrobial agents in parallel. The XGBoost model was then used for feature selection, and those features were used as input to the NN. The 29 selected features from the XGBoost model gave an F1 score of 0.808 when applied to a NN model. That NN model has a +-1 2-fold dilution accuracy of ׅ89 for all antibiotics. The results prove that the XGBoost model is capable of selecting important features from the dataset and those selected features can be used to generate a highly effective NN model that can predict MIC results.

Antibiotics

Medical Treatment

Artificial neural networks

Dropout

F1 score

Feature extraction

feature selection

K-Mer

MIC

Microorganisms

Microwave integrated circuits

Neural Network

overfitting

Predictive models

XGBoost

Details

Title: Subtitle: Using Feature Selection from XGBoost to Predict MIC Values with Neural Networks
Creators: Cory Kromer-Edwards - University of Iowa
Mariana Castanheira - JMI Laboratories
Suely Oliveira - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2023 International Joint Conference on Neural Networks (IJCNN), pp.1-8
Publisher: IEEE
DOI: 10.1109/IJCNN54540.2023.10191713
eISSN: 2161-4407
Language: English
Date published: 06/18/2023
Academic Unit: Computer Science; Mathematics
Record Identifier: 9984457960102771

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