In this chapter, we've seen two examples of the application of neural networks to disease diagnosis. The fundamentals of the classification problems are briefly reviewed in order to level the knowledge explored in this chapter. Classification tasks belong to one of the most frequently used types of supervised tasks in the fields of machine learning/data mining, and neural networks proved to be very appropriate for application to such problems. The reader was also presented with the concepts used for evaluating the classification tasks, such as sensitivity, specificity, and the confusion matrix. These notations are very useful for all classification tasks, including those that are handled with other algorithms besides neural networks. The next chapter will explore a similar kind of tasks but by using unsupervised learning, which means without expected output data, but the fundamentals presented in this chapter will be somewhat helpful.