Volatile Organic Compounds for the Prediction of Lung Cancer by Using Ensembled Machine Learning Model and Feature Selection

The advancement of biomarkers is critically important at present, as lung cancer is a leading cause of death. In the present study, volatile organic compounds (VOCs) are considered as biomarkers to predict lung cancer. VOCs from seven different sources including breath, blood, urine, cell line, plerual fluid, cancer tissue and lung tissue are targeted to enhance the prediction reliability. In the present study, major focus is on the feature selection phase and model fusion phase. Five in-built and one proposed ensemble machine learning models have been utilised to investigate the different types of VOCs. The idea behind designing of one ensemble model is that the prediction results of individual models by using optimal feature sets are not satisfactory. This reason leads to designing of ensemble model to predict breath VOCs. The AvNNet model has superior performance in predicting blood VOCs, cancer tissue VOCs, cell line VOCs, and urine VOCs compared to four other models, achieving accuracies of 70%, 80%, 70%, and 90% accordingly on the validation dataset. The Blackboost model achieved 90% accuracy on the validation dataset in its prediction of lung tissue VOCs. With 90% accuracy on a validation dataset, the random forest model predicts pleural fluid volatile organic compounds efficiently. When compared to individual models, the proposed ensemble model predicts breath VOCs more effectively and achieves 100% accuracy on the validation dataset.