Classification models for detection of lung cancer based on nine element distribution of urine samples

The detection of lung cancer has a special value in the diagnosis of cancer diseases. Based on nine elemental concentrations (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel, and selenium) in urine samples and an ensemble linear discriminant analysis (ELDA), a detection method for lung cancer has been developed. A dataset containing 30 healthy samples and 27 lung cancer samples is used for experiment. The whole dataset was first split into a training set with 29 samples and a test set with 28 samples. The prediction results from the ELDA classifier were compared with those from single Fisher's discriminate analysis (FDA). On the test set, the ELDA classifier achieved better performance, that is, a sensitivity of 100%, a specificity of 86.7%, and an overall accuracy of 92.9%, while the FDA classifier had a sensitivity of 92.3%, a specificity of 93.3%, and an overall accuracy of 92.9%. The superiority of ELDA to FDA is ascribed to the fact that ELDA can model more nonlinear relationships through the cooperation of several single models, suggesting that ensemble modeling is more advisable in such a task.