Fast algorithm of support vector machines in lung cancer diagnosis

In this paper a method of lung cancer aid diagnosis using support vector machines is proposed. Combined with the knowledge of pathology, the improvement of sequential minimal optimization (SMO) is achieved by the introduction of game theory to accelerate the training process. The experimental result shows that the speed increased greatly. And comparing with other systems, the diagnosis identification rate of the three main kinds of cancer cells is also increased.