Early Detection of Lung Cancer Through Breath Analysis Using AdaBoost Ensemble Learning Method

Abstract

This pilot study presents the application of the ensemble learning method, AdaBoost in the detection of early-stage lung cancers. To detect the presence and variations of volatile organic compound biomarkers in the expelled breath, an electronic nose system with metal oxide gas sensors is developed. The system is tested in ten lung cancer patients and fifteen healthy controls to differentiate the breath samples. The system attained an acceptable accuracy, sensitivity, and specificity of 76 %, 70 %, and 80 % respectively with an independent component analysis (ICA) dimensionality reduction technique. The system should be further studied with adequate number of early stage cancers to get a concluding remark about the performance of the system in the detection of early-stage lung cancers.