An enhanced machine learning framework for accurate diagnosis of tuberculous pleural effusion

In this paper, we propose an improved SMA algorithm (SRSMA), incorporating a Sobel sequence mechanism and Rosenbrock mechanism. The next task involves constructing a machine learning model called SRSMA-FKNN to predict tuberculous pleural effusion by combining SRSMA with Fuzzy k-nearest neighbor (FKNN). To verify the capability of the new algorithm, using 30 IEEE CEC2017 competition functions, comparative experiments are carried out on nine classical meta-heuristic algorithms, eleven improved algorithms, and SRSMA. The findings of the experiments with the mean, standard deviation, Friedman test, and Wilcoxon signed rank test demonstrated that SRSMA was the most successful method overally. To further validate the utility of our proposed SRSMA-FKNN framework, we conducted experiments using clinical data collected from patients with suspected tuberculous pleural effusion. Our results indicate that key indicators, such as pleural effusion adenosine deaminase (PEADA), glucose (GLU), urea nitrogen/creatinine ratio (BUN/CR), lactate dehydrogenase (LDH), and pleural effusion lymphocytes percentage (PELP) are critical for the feature selection suggested in this study to determine the severity of tuberculous pleural effusion. According to the classification results, the prediction model demonstrates an accuracy of 88.57%, with a sensitivity of 92.47%, a precision of 86.92%, a specificity of 85.62%, and a Matthews correlation coefficient (MCC) of 77.40%. These results highlight the potential of our SRSMA-FKNN model as a powerful tool for accurate and timely diagnosis of tuberculous pleural effusion.