Multi-Label Classification for Predicting Antimicrobial Resistance on E. coli
Abstract
:1. Introduction
2. Predicting Antimicrobial Resistance
2.1. Data Collection
2.2. Data Preprocessing
2.3. Prediction Models
2.3.1. Decision Tree
2.3.2. Random Forest
2.3.3. eXtreme Gradient Boosting
2.3.4. Categorical Boosting
2.3.5. Adaptive Gradient Boosting
2.3.6. Gradient Boosted Classifier
2.3.7. Extra Trees Classifier
2.3.8. k_Nearest Neighbors
2.3.9. Support Vector Machine
2.3.10. Logistic Regression
2.3.11. Multinomial Naive Bayes
3. Experimentation
3.1. Dataset Description
3.2. Experimental Setup
3.3. Hyperparameter Setting
3.4. Evaluation Metrics
3.5. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- World Health Organization. WHO Outlines 40 Research Priorities on Antimicrobial Resistance. Available online: https://www.who.int/news/item/22-06-2023-who-outlines-40-research-priorities-on-antimicrobial-resistance (accessed on 25 March 2024).
- Centers for Disease Control and Prevention. Antimicrobial Resistance. Available online: https://www.cdc.gov/antimicrobial-resistance/ (accessed on 25 March 2024).
- Murray, C.J.; Ikuta, K.S.; Sharara, F.; Swetschinski, L.; Aguilar, G.R.; Gray, A.; Han, C.; Bisignano, C.; Rao, P.; Wool, E.; et al. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 2022, 399, 629–655. [Google Scholar] [CrossRef] [PubMed]
- Aalinezhad, M.; Rezaei, M.H.; Alikhani, F.; Akbari, P.; Soleimani, S.; Hakamifard, A. Relationship between CT Severity Score and Capillary Blood Oxygen Saturation in Patients with COVID-19 Infection. Indian J. Crit. Care Med. 2021, 25, 279–283. [Google Scholar] [CrossRef] [PubMed]
- Nwobodo, D.C.; Ugwu, M.C.; Anie, C.O.; Al-Ouqaili, M.T.S.; Ikem, J.C.; Chigozie, U.V.; Saki, M. Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace. J. Clin. Lab. Anal 2022, 36, e24655. [Google Scholar] [CrossRef]
- Azabo, R.; Dulle, F.; Mshana, S.E.; Matee, M.; Kimera, S. Antimicrobial use in cattle and poultry production on occurrence of multidrug resistant Escherichia coli. A systematic review with focus on sub-Saharan Africa. Front. Vet. Sci. 2022, 9, 1000457. [Google Scholar]
- Ali, T.; Ahmed, S.; Aslam, M. Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics 2023, 12, 523. [Google Scholar] [CrossRef]
- Tsoumakas, G.; Katakis, I. Multi-label classification: An overview. Int. J. Data Warehous. Min. 2007, 3, 1–13. [Google Scholar] [CrossRef]
- Tarekegn, A.N.; Giacobini, M.; Michalak, K. A review of methods for imbalanced multi-label classification. Pattern Recognit. 2021, 118, 107965. [Google Scholar] [CrossRef]
- Feucherolles, M.; Nennig, M.; Becker, S.L.; Martiny, D.; Losch, S.; Penny, C.; Cauchie, H.M.; Ragimbeau, C. Investigation of MALDI-TOF Mass Spectrometry for Assessing the Molecular Diversity of Campylobacter jejuni and Comparison with MLST and cgMLST: A Luxembourg One-Health Study. Diagnostics 2021, 11, 1949. [Google Scholar] [CrossRef]
- Wang, G.; Vaisman, I.I.; van Hoek, M.L. Machine Learning Prediction of Antimicrobial Peptides. In Computational Peptide Science: Methods and Protocols; Springer: New York, NY, USA, 2022; Volume 2405, pp. 1–37. [Google Scholar]
- Yang, Y.; Niehaus, K.E.; Walker, T.M.; Iqbal, Z.; Walker, A.S.; Wilson, D.J.; Peto, T.E.; Crook, D.W.; Smith, E.G.; Zhu, T.; et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics 2018, 34, 1666–1671. [Google Scholar] [CrossRef]
- Kouchaki, S.; Yang, Y.; Walker, T.M.; Walker, A.S.; Wilson, D.J.; Peto, T.E.A.; Crook, D.W.; CRyPTIC Consortium; Clifton, D.A. Application of machine learning techniques to tuberculosis drug resistance analysis. Bioinformatics 2019, 35, 2276–2282. [Google Scholar] [CrossRef]
- Söylemez, Ü.G.; Yousef, M.; Kesmen, Z.; Büyükkiraz, M.E.; Bakir-Gungor, B. Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. Appl. Sci. 2022, 12, 3631. [Google Scholar] [CrossRef]
- Ren, Y.; Chakraborty, T.; Doijad, S.; Falgenhauer, L.; Falgenhauer, J.; Goesmann, A.; Schwengers, O.; Heider, D. Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics. Antibiotics 2022, 11, 1611. [Google Scholar] [CrossRef] [PubMed]
- Ren, Y.; Chakraborty, T.; Doijad, S.; Falgenhauer, L.; Falgenhauer, J.; Goesmann, A.; Schwengers, O.; Heider, D. Multi-label classification for multi-drug resistance prediction of Escherichia coli. Comput. Struct. Biotechnol. J. 2022, 20, 1264–1270. [Google Scholar] [CrossRef] [PubMed]
- Okerinde, A.; Shamir, L.; Hsu, W.; Theis, T.; Nafi, N. eGAN: Unsupervised approach to class imbalance using transfer learning. In Computer Analysis of Images and Patterns, Proceedings of the 19th International Conference, CAIP 2021, Virtual Event, 28–30 September 2021; Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M., Eds.; Springer International Publishing: Cham, Switzerland, 2021. [Google Scholar]
- Minvielle, L.; Atiq, M.; Peignier, S.; Mougeot, M. Transfer Learning on Decision Tree with Class Imbalance. In Proceedings of the IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 4–6 November 2019; pp. 1003–1010. [Google Scholar]
- Gao, Y.; Cui, Y. Author Correction: Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nat. Commun. 2020, 11, 6444. [Google Scholar] [CrossRef]
- Moradigaravand, D.; Palm, M.; Farewell, A.; Mustonen, V.; Warringer, J.; Parts, L. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLOS Comput. Biol. 2018, 14, e1006258. [Google Scholar] [CrossRef]
- Ren, Y.; Chakraborty, T.; Doijad, S.; Falgenhauer, L.; Falgenhauer, J.; Goesmann, A.; Hauschild, A.-C.; Schwengers, O.; Heider, D. Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics 2021, 38, 325–334. [Google Scholar] [CrossRef]
- Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M.; et al. Twelve years of SAMtools and BCFtools. GigaScience 2021, 10, giab008. [Google Scholar] [CrossRef]
- Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
- Breiman, L.; Friedman, J.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; Routledge: Abingdon, UK, 2017. [Google Scholar]
- Collins, M.; Schapire, R.E.; Singer, Y. Logistic Regression, AdaBoost and Bregman Distances. Mach. Learn. 2002, 48, 253–285. [Google Scholar] [CrossRef]
- Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 27–29 July 1992; pp. 144–152. [Google Scholar] [CrossRef]
- Burges, C.J.C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov. 1998, 2, 121–167. [Google Scholar] [CrossRef]
- Mangasarian, O.L.; David, R.M. Lagrangian support vector machines. J. Mach. Learn. Res. 2001, 1, 161–177. [Google Scholar]
- Arana-Daniel, N.; Gallegos, A.A.; López-Franco, C.; Alanís, A.Y.; Morales, J.; López-Franco, A. Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures. Evol. Bioinform. 2016, 12, EBO.S40912–302. [Google Scholar] [CrossRef] [PubMed]
- Xu, J. Multi-label Lagrangian support vector machine with random block coordinate descent method. Inf. Sci. 2016, 329, 184–205. [Google Scholar] [CrossRef]
- Hosmer, D.W., Jr.; Lemeshow, S.; Sturdivant, R.X. Applied Logistic Regression; John Wiley & Sons: Hoboken, NJ, USA, 2013. [Google Scholar]
- Lewis, C.M.; Knight, J. Introduction to Genetic Association Studies. Cold Spring Harb. Protoc. 2012, 2012, 297–306. [Google Scholar] [CrossRef] [PubMed]
- Lowrie, E.G.; Lew, N.L. Death Risk in Hemodialysis Patients: The Predictive Value of Commonly Measured Variables and an Evaluation of Death Rate Differences Between Facilities. Am. J. Kidney Dis. 1990, 15, 458–482. [Google Scholar] [CrossRef]
- Langer, D.L.; Van der Kwast, T.H.; Evans, A.J.; Trachtenberg, J.; Wilson, B.C.; Haider, M.A. Prostate cancer detection with multi-parametric MRI: Logistic regression analysis of quantitative T2, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 2009, 30, 327–334. [Google Scholar] [CrossRef]
- Rennie, J.D.; Shih, L.; Teevan, J.; Karger, D.R. Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (ICML-03), Los Angeles, CA, USA, 23–24 June 2003. [Google Scholar]
- Maxwell, A.; Li, R.; Yang, B.; Weng, H.; Ou, A.; Hong, H.; Zhou, Z.; Gong, P.; Zhang, C. Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinform. 2017, 18, 523. [Google Scholar] [CrossRef]
- Powers, D.M. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv 2020, arXiv:2010.16061. [Google Scholar]
- Schapire, R.E.; Singer, Y. BoosTexter: A Boosting-based System for Text Categorization. Mach. Learn. 2000, 39, 135–168. [Google Scholar] [CrossRef]
- Nsubuga, M.; Galiwango, R.; Jjingo, D.; Mboowa, G. Generalizability of machine learning in predicting antimicrobial resistance in E. coli: A multi-country case study in Africa. BMC Genom. 2024, 25, 287. [Google Scholar] [CrossRef]
- Amjad, M.; Ahmad, I.; Ahmad, M.; Wróblewski, P.; Kamiński, P.; Amjad, U. Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation. Appl. Sci 2022, 12, 2126. [Google Scholar] [CrossRef]
- Can, R.; Kocaman, S.; Gokceoglu, C.A. Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey. Appl. Sci 2021, 11, 4993. [Google Scholar] [CrossRef]
- Asselman, A.; Khaldi, M.; Aammou, S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interact. Learn. Environ. 2021, 31, 3360–3379. [Google Scholar] [CrossRef]
Models | Parameters | Description | Value |
---|---|---|---|
DT | Max_depth | Maximum depth of the tree | 0.1 |
Min_samples_split | Minimum number of samples required to split a node | 6 | |
Min_samples_leaf | Minimum number of samples required to be at a leaf node | 100 | |
RF | N_estimators | Number of trees in the forest | 100 |
Max_depth | Maximum depth of the tree | None | |
XGBoost | Learning rate | Step size shrinkage used in updating the weights | 0.1 |
Depth | Maximum depth of a tree | 6 | |
N_estimators | Number of trees in the forest | 100 | |
CatBoost | Iterations | Number of boosting rounds | 100 |
Learning rate | Step size shrinkage used in updating the weight. | 0.1 | |
Depth | Maximum depth of a tree | 6 | |
AdaBoost | Learning rate | Step size shrinkage used in updating the weights | 0.1 |
GBC | Learning rate | Step size shrinkage used in updating the weights | 0.1 |
ETC | N_estimators | Number of trees in the forest | 100 |
Max_depth | Maximum depth of the tree | None | |
Min_samples_split | Minimum number of samples required to split a node | 2 | |
Min_samples_leaf | Minimum number of samples required to be at a leaf node | Auto | |
k-NN | Solver | Optimization algorithm used for training. | Adam |
SVM | C | Regularization parameter | 1 |
kernel | Specifies the kernel type used in the algorithm | rbf | |
gamma | Kernel coefficient for rbf to determine influence range of a single data point | scale | |
LR | Max_iter | Maximum number of iterations for solvers to converge | 1000 |
MNB | Learning rate | Step size shrinkage used in updating the weights | 0.1 |
Models | Accuracy | Precision | Recall | F1-score | HAM |
---|---|---|---|---|---|
DT | 0.849 | 0.850 | 0.849 | 0.849 | 0.151 |
RF | 0.885 | 0.887 | 0.885 | 0.885 | 0.115 |
XGBoost | 0.890 | 0.891 | 0.890 | 0.890 | 0.110 |
CatBoost | 0.878 | 0.882 | 0.878 | 0.878 | 0.122 |
AdaBoost | 0.871 | 0.872 | 0.871 | 0.871 | 0.129 |
GBM | 0.883 | 0.884 | 0.883 | 0.883 | 0.117 |
ETC | 0.886 | 0.888 | 0.886 | 0.886 | 0.114 |
k-NN | 0.884 | 0.845 | 0.844 | 0.845 | 0.156 |
SVM | 0.842 | 0.846 | 0.842 | 0.842 | 0.158 |
LR | 0.875 | 0.875 | 0.875 | 0.874 | 0.125 |
MNB | 0.587 | 0.629 | 0.587 | 0.565 | 0.413 |
Authors | Models | Accuracy | Precision | Recall | F1-score | HAM |
---|---|---|---|---|---|---|
Yunxiao et al. [15] | Deep TL | 0.915 | 0.890 | 0.930 | 0.830 | - |
Yunxiao et al. [16] | ECC (RF) | 0.72 | 0.890 | 0.94 | 0.98 | 0.11 |
Yunxiao et al. [21] | CNN | 0.870 | 0.710 | 0.890 | - | - |
Proposed Approach | XGBoost | 0.890 | 0.891 | 0.890 | 0.890 | 0.110 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gidiglo, P.D.; Ngnamsie Njimbouom, S.; Aly Abdelkader, G.; Mosalla, S.; Kim, J.-D. Multi-Label Classification for Predicting Antimicrobial Resistance on E. coli. Appl. Sci. 2024, 14, 8225. https://doi.org/10.3390/app14188225
Gidiglo PD, Ngnamsie Njimbouom S, Aly Abdelkader G, Mosalla S, Kim J-D. Multi-Label Classification for Predicting Antimicrobial Resistance on E. coli. Applied Sciences. 2024; 14(18):8225. https://doi.org/10.3390/app14188225
Chicago/Turabian StyleGidiglo, Prince Delator, Soualihou Ngnamsie Njimbouom, Gelany Aly Abdelkader, Soophia Mosalla, and Jeong-Dong Kim. 2024. "Multi-Label Classification for Predicting Antimicrobial Resistance on E. coli" Applied Sciences 14, no. 18: 8225. https://doi.org/10.3390/app14188225