Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine

doi:10.1016/j.compbiomed.2023.106948

科研成果详情

题名	Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine
作者	Yang, Song1; Lou, Lejing1; Wang, Wangjia1; Li, Jie 2; Jin, Xiao1; Wang, Shijia1; Cai, Jihao 3; Kuang, Fangjun 4; Liu, Lei 5; Hadjouni, Myriam 6; Elmannai, Hela 7; Cai, Chang1
发表日期	2023-07
发表期刊	Computers in biology and medicine 影响因子和分区
语种	英语
原始文献类型	Journal Article ; Research Support, Non-U.S. Gov't
关键词	ACO Ant colony optimizer Feature selection Pneumothorax Support vector machine Swarm intelligence
其他关键词	OBJECTIVE DEPLOYMENT OPTIMIZATION ; GLOBAL OPTIMIZATION ; DIFFERENTIAL EVOLUTION ; BIOPSY ; ALGORITHM ; COMPLICATIONS ; INTELLIGENCE ; SEARCH ; TESTS
摘要	Although PNLB is generally considered safe, it is still invasive and risky. Pneumothorax, the most common complication of lung puncture, can cause shortness of breath, chest pain, and even life-threatening. Therefore, the auxiliary diagnosis for pneumothorax is of great clinical interest. This paper proposes an ant colony optimizer with slime mould foraging behavior and collaborative hunting, called SCACO, in which slime mould foraging behavior is combined to improve the convergence accuracy and solution quality of ACOR. Then the ability of ACO to jump out of the local optimum is optimized by an adaptive collaborative hunting strategy when trapped in the local optimum. As a first step toward Pneumothorax diagnostic prediction, we suggested an SVM classifier based on bSCACO (bSCACO-SVM), which uses the proposed SCACO's binary version as the basis for its feature selection algorithms. To demonstrate the SCACO performance, we first used the slime mould foraging behavior and adaptive cooperative hunting strategy, then compared SCACO with nine basic algorithms and nine variants, respectively. Finally, we verified bSCACO-SVM on various widely used public datasets and applied it to the Pneumothorax prediction issue, showing that it has robust classification prediction capacity and can be successfully employed for tuberculous pleural effusion diagnostic prediction.
资助项目	Ministry of Education[20YJA790090];Princess Nourah bint Abdulrahman University[PNURSP2023R193];Wenzhou Science & Technology Bureau[ZG2020030];
出版者	Elsevier Ltd
ISSN	0010-4825
EISSN	1879-0534
卷号	161
DOI	10.1016/j.compbiomed.2023.106948
页数	28
WOS类目	Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
WOS记录号	WOS:001001691000001
收录类别	PUBMED ; EI ; SCIE ; SCOPUS
在线发表日期	2023-05
EI入藏号	20232014105667
EI主题词	Forecasting
EI分类号	716.1 Information Theory and Signal Processing ; 723 Computer Software, Data Handling and Applications ; 723.4 Artificial Intelligence ; 903.1 Information Sources and Analysis ; 921.5 Optimization Techniques
URL	查看原文
PubMed ID	37207406
SCOPUSEID	2-s2.0-85159328010
通讯作者地址	[Liu, Lei]College of Computer Science,Sichuan University,Sichuan,Chengdu,610065,China ; [Kuang, Fangjun]School of Information Engineering,Wenzhou Business College,Wenzhou,325035,China ; [Cai, Chang]Department of Respiratory and Critical Care Medicine,The First Affiliated Hospital,Wenzhou Medical University,Wenzhou,China
Scopus学科分类	Health Informatics;Computer Science Applications
TOP期刊	TOP期刊
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/180532
专题	第一临床医学院（信息与工程学院）、附属第一医院附属第一医院仁济学院
通讯作者	Kuang, Fangjun; Liu, Lei; Cai, Chang
作者单位	1.Department of Respiratory and Critical Care Medicine,The First Affiliated Hospital,Wenzhou Medical University,Wenzhou,China; 2.Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province,The First Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China; 3.Wenzhou Medical University Renji College,Wenzhou,China; 4.School of Information Engineering,Wenzhou Business College,Wenzhou,325035,China; 5.College of Computer Science,Sichuan University,Sichuan,Chengdu,610065,China; 6.Department of Computer Sciences,College of Computer and Information Science,Princess Nourah bint Abdulrahman University,P.O. Box 84428,Riyadh,11671,Saudi Arabia; 7.Department of Information Technology,College of Computer and Information Science,Princess Nourah bint Abdulrahman University,P.O. Box 84428,Riyadh,11671,Saudi Arabia
第一作者单位	第一临床医学院（信息与工程学院）、附属第一医院; 附属第一医院
通讯作者单位	第一临床医学院（信息与工程学院）、附属第一医院; 附属第一医院
第一作者的第一单位	第一临床医学院（信息与工程学院）、附属第一医院; 附属第一医院
推荐引用方式 GB/T 7714	Yang, Song,Lou, Lejing,Wang, Wangjia,et al. Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine[J]. Computers in biology and medicine,2023,161.
APA	Yang, Song., Lou, Lejing., Wang, Wangjia., Li, Jie., Jin, Xiao., ... & Cai, Chang. (2023). Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine. Computers in biology and medicine, 161.
MLA	Yang, Song,et al."Pneumothorax prediction using a foraging and hunting based ant colony optimizer assisted support vector machine".Computers in biology and medicine 161(2023).