Lung cancer is accountable for more deaths each year than all of the other leading cancers - breast, colon and prostate diseases – combined, and is the leading cause of cancer death worldwide. A major problem to manage this disease is that there are no detectable symptoms early on and most of the time the disease is diagnosed at later and more advanced stage when it can no longer be treated effectively. Traditional screening modalities (chest x-ray and sputum cytology) have shown poor success rates and although improvements in imaging/staging technology have occurred, five year survival rates are still not high. Machine learning (ML) and artificial intelligence (AI) algorithms are emerging as a potential solution to enhance the early detection, classification and prediction of lung cancer status. Several recent ML models such as deep learning, support vector machines and ensemble models for the identification and prediction of lung cancer are explored. We report their performance, data sets investigated, and shortcomings and discuss these findings in the context of future work directed towards clinically useful diagnostic systems.
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