Assessing the Utility of Logistic Regression Models for Pulmonary Effusion Diagnosis

Logistic regression, a classifier algorithm that estimates the chance of an event being genuine, is widely employed in lots of fields in guide of selection making. In this look at, we discover the application of logistic regression fashions in the analysis of pulmonary effusions. We educate and examine logistic regression models with a Cagle dataset containing clinical photographs of numerous radiologic research, from which we extract the applicable features for prognosis. We compare our fashions with different well known gadget studying algorithms, like SVM and okay-NN, to decide the effectiveness for pulmonary effusion diagnosis. The effects of the assessment confirmed that, logistic regression had better accuracy, sensitivity, and specificity as compared to the opposite algorithms. These outcomes highlight the ability use of logistic regression fashions for pulmonary effusion prognosis to improve the accuracy of clinical choices.