Lung cancer detection via breath by electronic nose enhanced with a sparse group feature selection approach

In the diagnosis of lung cancer, electronic nose (E-nose) has attracted much attention by detecting the volatile organic compounds (VOCs) in exhaled breath. In this research, an E-nose platform with a novel thermal desorption preconcentration subsystem is designed to verify whether analyzing VOCs can reliably differentiate lung cancer patients from healthy individuals and patients with benign pulmonary diseases. To this end, total 87 subjects (46 patients with lung cancer, 36 healthy volunteers and 5 patients with benign pulmonary diseases) are enrolled for the sensor array data collection. 13 composite features are extracted from each sensor, and some classical classifiers are established to demonstrate the feasibility of identifying lung cancer patients through VOCs. To improve the performance, considering the inherent characteristics of E-nose data, a sparse group feature selection (FS) method is applied to the raw sensor array data. It is observed that the FS method can reduce data dimensionality and improve the classification performance significantly. Statistical analysis for the effect of age and smoking habit on classification results shows that no significant influences are found.