Performance Comparasion of Different Hybrid Approaches for Lung Cancer Recurrence Based on Supervised Learning with Ensemble Techniques

The paper is about the comparative analysis of different statistical machine learning algorithms for lung cancer recurrence based on non-small cell lung cancer carcinoma microarray data. Various approaches have been taken to predict about the cancer recurrence possibility to a patient who has gone through some treatment. Machine learning is a branch of artificial intelligence which has been used widely in field of health care. The performance comparasion of various statistical approaches is done on the basis of several statistical parameters such as prediction accuracy, specificity, sensitivity and AUROC. The approach to solve the problem consists of basic four steps: gene selection, designing classifier model, applying the classifier model on test dataset, statistical parameter finding and finally the comparison of the results. For gene selection and classification both, hybrid approach is used. The classification techniques are combined with optimization techniques to achieve better performance.