An intelligent system for the classification of postoperative pleural effusion between 4 and 30 daysusing medical knowledge discovery.

Abstract

Objective: Pleural Effusion (PE) is a considerable and a common health problem. The classification of this condition is of great importance in terms of clinical decision making. The purpose of the study is to design an intelligent system for the classification of postoperative pleural effusion between 4 and 30 days after surgery by medical knowledge discovery (MKD) methods.

Materials and methods: This study included 2309 individuals diagnosed with coronary artery disease for elective coronary artery bypass grafting (CABG) operation. The results of chest x-ray were used to diagnose PE. The subjects were allocated to two groups: PE group (n=81) and non-PE group (n=2228), consecutively. In the preprocessing step, outlier analysis, data transformation and feature selection processes were performed. In the data mining step, Naive Bayes, Bayesian network and Random Forest algorithms were utilized. Accuracy and area under receiver operating characteristics (ROC) curve (AUC) were calculated as evaluation metrics.

Results: In the preprocessing step, 85 outlier observations were removed from the study. The rest of the data consisted of 2224 subjects: 2149 of these individuals were in non-PE group, and the 75 were in PE group. Random Forest yielded the best classification performance with 97.45% of accuracy and 0.990 of AUC for 0.7 of the optimal split ratio by Grid search algorithm.

Conclusion: The achieved results pointed out that the best classification performance was obtained from the RF ensemble model. Therefore, the suggested intelligent system can be used as a clinical decision making tool.