Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis
Alfakeeh, A., Sharif, S., Zorto, A. and Pillonetto, T. 2023. Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis. Applied Computational Intelligence and Soft Computing. In Press.
|Authors||Alfakeeh, A., Sharif, S., Zorto, A. and Pillonetto, T.|
Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical data sets for patients diagnosed with sepsis, it analyses the efficacy of ensemble machine learning techniques compared to non ensemble machine learning techniques and the significance of data balancing and Conditional Tabular Generative Adversarial Nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the non-ensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90 and an accuracy of 90%. Histogram-based Gradient Boosting Classification Tree achieved an F score of 0.96, an AUC of 0.96 and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state of the art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and Conditional Tabular Generative Adversarial Nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.
|Journal||Applied Computational Intelligence and Soft Computing|
|Journal citation||In Press|
|Publisher||Hindawi Publishing Corporation|
|Accepted author manuscript|
File Access Level
|Publication process dates|
|Accepted||07 Nov 2023|
|Deposited||15 Nov 2023|
|Copyright holder||© 2023, The Authors|
|Copyright information||Authors retain the copyright of their manuscripts, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.|
0views this month
0downloads this month