Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Abstract

Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia-red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.

Fig 1. The flowchart of the proposed method.

Fig 2. The structure of the used RQBSO algorithm.

Fig 3. The pseudocode of the used RQBSO algorithm.

Fig 4

(a) solutions generated by the first strategy, (b) solutions generated by the second strategy.

Fig 5. Comparison of ROC and PRC curve of RQBSO and other algorithms.

(a, b) correspond to the ROC curve of dataset 1 and dataset 2. The numbers in parentheses indicate the AUROC value. The x-axis represents sensitivity, or true positive rate (TPR). The y-axis is 1-Specificity, or false positive rate (FPR). (c, d) represents the PRC curve of dataset 1 and dataset 2. The numbers in parentheses indicate the AUPRC value. The x-axis represents recall. The y-axis is precision.

Fig 6. Comparison of ROC and PRC curve of different classifiers.

(a, b) correspond to the ROC curve of dataset 1 and dataset 2. The numbers in parentheses indicate the AUROC value. The x-axis represents sensitivity, or true positive rate (TPR). The y-axis is 1-Specificity, or false positive rate (FPR). (c, d) represents the PRC curve of dataset 1 and dataset 2. The numbers in parentheses indicate the AUPRC value. The x-axis represents recall. The y-axis is precision.