Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-25 DOI:10.1155/2023/8819052
A. Alfakeeh, M. S. Sharif, A. Zorto, Thiago Pillonetto
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Abstract

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.
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用于增强败血症诊断中的集合分类器的条件表生成对抗网
由于抗生素的广泛使用和新药物研究的匮乏,抗生素耐药细菌以惊人的速度激增。抗生素耐药细菌感染发展成败血症的可能性是影响败血症患者的主要附带问题之一。在英国,有 31,000 人因败血症而丧生,每年的损失约为 20 亿英镑。这项研究旨在开发和评估几种分类方法,以改进败血症的预测,减少计算机辅助预测工具中诊断不足的倾向。这项研究采用了被诊断为败血症患者的医疗数据集,分析了集合机器学习技术与非集合机器学习技术的功效对比,以及数据平衡和条件表生成对抗网的数据增强在产生可靠诊断方面的意义。本文中训练的非集合模型获得的平均 F 分数为 0.83,而集合技术的平均 F 分数为 0.94。非集合技术(如决策树)的 F 得分为 0.90,AUC 为 0.90,准确率为 90%。基于直方图的梯度提升分类树的 F 值为 0.96,AUC 为 0.96,准确率为 95%,超过了其他测试模型。此外,与目前最先进的败血症预测模型相比,本研究中开发的模型在所有指标上都表现出更高的平均性能,这表明通过数据平衡和条件表生成对抗网进行数据增强,减少了偏差并提高了鲁棒性。研究表明,在集合机器学习算法上进行数据平衡和增强可提高临床预测模型的功效,并能帮助诊所在检查病人时决定哪些数据类型最重要,通过智能人机界面及早诊断败血症。
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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