Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

Abstract

has spread rapidly across the world, leading to the insufficiency of medical resources in many regions. Early detection and identification of high-risk COVID-19 patients will contribute to early intervention and optimize medical resource allocation. Using the clinical data from the Affiliated Yueqing Hospital of Wenzhou Medical University (Yueqing, China), an evolutionary support vector machine model is designed to recognize and discriminate the severity of the COVID-19 by patients basic information and hematological indexes. The support vector machine is a frequently used pattern classification tool affected by both the kernel parameter setting and feature selection for its classification accuracy. This study recommends an enhanced Slime Mould Algorithm (ESMA), mixing a new movement strategy of white holes, black holes, and wormholes, to perform parameter optimization and feature selection simultaneously for SVM. Therefore, the proposed SVM framework (ESMA-SVM) can also obtain highquality classification results, and it is less prone to stagnation in the classification process. To verify the capabilities of the proposed methodology, first, the performance of the ESMA is thoroughly verified by using IEEE CEC2017 benchmark functions and the diversity and compared with other similar methods experimentally using these standard benchmark functions. Moreover, the balance between diversification and intensification capability of the enhanced ESMA and the original SMA is also investigated statistically. Finally, the designed model ESMA-SVM and other competitive SVM models based on other optimization algorithms are applied to early recognition and discrimination of COVID-19 severity. Through the analysis of experimental results, the core compensations of ESMA are confirmed, and the ESMA-SVM can obtain strong performance in terms of several performance evaluation indexes on discrimination of COVID-19 severity.

The Efficiency of Inter-provincial Health Resource Allocation and its Influencing Factors in China---An Empirical Study Based on DEA Method and a Spatial Econometric Model

“…The result shows that the spatial distribution of the efficiency of medical and health resource allocation in China is not random and some provinces tend to converge in the geographical space. Thus, Chinese health departments (36) should reasonably adjust the allocation of high-quality medical resources and focus on solving problems of redundancy or insufficiency of medical resource allocation (37) in provinces. The efficiency of resource allocation needs to be improved in order to match the demand for high-quality medical service.…”

Digitalization of prevention and treatment and the combination of western and Chinese medicine in management of acute heart failure

“…Multiple studies have shown that many patients remain concerned about the security of their medical information and may lack confidence in the ability of current technology to protect their privacy ( 41 ). Efficient and secure storage of data will be the next challenge; the processes of using this data should also comply with the informed consent of the owner, and data should be desensitized for use in order to avoid social problems such as discrimination ( 42 ). The advancement of these processes will also involve high economic costs and problems related to the improvement of relevant laws and regulations, which need to be addressed in the context of medical science.…”

Design of Covid19 Disease Detection for Risk Identification using Deep Learning Approach

“…They have been used to examine the show of classification models, including DL techniques. Hybrid operational strategy called the Enhanced Slim Mold Algorithm (ESMA) can unambiguously detect parameter optimization and feature selection for white holes, black holes, and wormholes in SVMs simultaneously [12]. The ESMA-SVM framework helps to reduce the chances of stagnation in the classification process and obtain high-quality classification results [13].…”