**7. Conclusion**

In the process of creation of a new medical application model we have inserted some elements of fuzzy systems into the negative selection immunological algorithm. This hybrid, attached to two disciplines of Computational Intelligence, has found a practical application in surgery decision making. As self and non-self constitute two regions of the NS partition of objects then we could identify these regions with decisions "operate" against "do not operate" in the case of curing gastric cancer patients. The action of the modified NS could help us to determine the surgery or its lack for individual patients with respect to their clinical data entry vectors.

To make the action of the NS algorithm more efficient we have complemented the method by preparing the population of the most representative vectors standing for the "operate" type. The vectors have been converted to real values giving the common characteristics of a patient. In that characteristics the weights of importance, assigned to biological markers, will play the essential role in the final judgment of the vectors' influence on the decision "operate".

We wish to add that the excerpts from fuzzy systems, involved in NS, come from own research, which has been concentrated on the creation of compact parametric formulas. These formulas concern the generation of a family of membership functions without predetermining their borders in advance.

All parts of the methodology have been prepared in the form of numerical algorithms given by flow charts. This allows composing a common computer program to test large samples of vectors in a real clinical application.

We emphasize that the proposal is a novel contribution in medical applications and should be still tested on larger samples of data. We can expect that, in future investigations, an introduction of the neural artificial perceptron model instead of the NS algorithm will provide us with similar results concerning surgery decisions. As an extension of the model we also wish to adapt the real-value negative selection algorithm in order to insert measured values of biological markers in data vectors instead of codes. This procedure should improve the reliability of a decision. Having results from more models we can select the most efficient one to work on its further development.
