Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis

• Published in: Artificial intelligence in medicine Vol. 153; p. 102886
• Main Authors: Zhou, Xinsen, Chen, Yi, Gui, Wenyong, Heidari, Ali Asghar, Cai, Zhennao, Wang, Mingjing, Chen, Huiling, Li, Chengye
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2024
• Subjects: Adenosine Deaminase - metabolism; Algorithms; Clinical characteristics analysis; Colony predation; Differential evolution; Dispersed foraging; Feature selection; Global optimization; Humans; Leukocyte Count; Machine learning; Pleural Effusion - microbiology; Support Vector Machine; Tuberculosis, Pleural - diagnosis; Tuberculous pleural effusion; Global optimization; Feature selection; Tuberculous pleural effusion; Colony predation; Clinical characteristics analysis; Machine learning; Differential evolution; Dispersed foraging
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2024.102886

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction. •The CFDE is designed and verified on benchmarks.•The binary version of CFDE (bCFDE) is designed for TBPE.•The bCFDE is verified on 24 datasets and compared to other peers.•The bCFDE-SVM is applied to TBPE successfully.