A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.
A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.
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DOI：
10.1109/access.2021.3050852
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Bairagi AK
中科院分区：
文献类型：
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作者：
Awal MA;Masud M;Hossain MS;Bulbul AA;Mahmud SMH;Bairagi AK
由于致命病毒COVID-19，全球面临大流行的情况。病毒成熟后需要相当长的时间才能被追踪，在此期间，它可能会在其他人之间传播。为了摆脱这种意外情况，需要快速识别COVID-19患者。我们使用住院患者的设施数据设计并优化了一个基于机器学习的框架，这将为这种流行病提供一个用户友好、具有成本效益和时间效率的解决方案。所提出的框架使用贝叶斯优化来优化分类器的超参数，并使用自适应合成（ADASYN）算法来平衡数据集的COVID和非COVID类。虽然所提出的技术已被应用到9个国家的最先进的分类器，以显示其有效性，它可以被用于许多分类器和分类问题。从本研究中可以明显看出，极限梯度增强（XGB）提供了97.00%的最高Kappa指数。与没有ADASYN相比，我们提出的方法在kappa指数上提高了96.94%。并将贝叶斯优化算法与网格搜索、随机搜索算法进行了比较，说明了贝叶斯优化算法的有效性.此外，最主要的功能已被确定使用SHAPELY自适应解释（SHAP）分析。并与其他相关著作作了比较。所提出的方法是能够足够的跟踪COVID患者花费更少的时间比传统的技术。最后，两个潜在的应用，即临床可操作的决策树和决策支持系统，已被证明支持临床工作人员和建立一个推荐系统。
The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient’s facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.