Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/8819052 |
| _version_ | 1797424798897274880 |
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| author | Ahmed Alfakeeh Mhd Saeed Sharif Abin Daniel Zorto Thiago Pillonetto |
| author_facet | Ahmed Alfakeeh Mhd Saeed Sharif Abin Daniel Zorto Thiago Pillonetto |
| author_sort | Ahmed Alfakeeh |
| collection | DOAJ |
| description | 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 datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble 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 nonensemble 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-basedgradient 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. |
| first_indexed | 2024-03-09T08:06:01Z |
| format | Article |
| id | doaj.art-f7331b11014343d9891ab39c2db6f629 |
| institution | Directory Open Access Journal |
| issn | 1687-9732 |
| language | English |
| last_indexed | 2024-03-09T08:06:01Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj.art-f7331b11014343d9891ab39c2db6f6292023-12-03T00:00:04ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/8819052Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis DiagnosisAhmed Alfakeeh0Mhd Saeed Sharif1Abin Daniel Zorto2Thiago Pillonetto3Research and Consultation InstituteSchool of Architecture Computing and Engineering (ACE)School of Architecture Computing and Engineering (ACE)School of Architecture Computing and Engineering (ACE)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 datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble 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 nonensemble 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-basedgradient 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.http://dx.doi.org/10.1155/2023/8819052 |
| spellingShingle | Ahmed Alfakeeh Mhd Saeed Sharif Abin Daniel Zorto Thiago Pillonetto Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis Applied Computational Intelligence and Soft Computing |
| title | Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis |
| title_full | Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis |
| title_fullStr | Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis |
| title_full_unstemmed | Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis |
| title_short | Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis |
| title_sort | conditional tabular generative adversarial net for enhancing ensemble classifiers in sepsis diagnosis |
| url | http://dx.doi.org/10.1155/2023/8819052 |
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