**4. Conclusion**

The data used in this study are from the Database of National Center of Oncology. Three years of case data of 100 patients are implemented for extracting the knowledge-based rule using clustering method. Veracity of 30 diagnoses of patients was checked, and 22 from them were defined as correct. In this chapter, for the evaluation of value of tumor response, a possibility-measure-based method is used. The created expert system for rectal cancer was implemented in the ESPLAN. Several tests were performed, and the outcomes were compared to the actual patient data.

The presentation of the developed system and samples of its use in medicine demonstrate that it has a wide range of potential capabilities for making decisions based on fuzzy information under uncertain circumstances. Experimental findings demonstrate the effectiveness of the proposed intelligent system.

In the future, we are planning to study and compare different types of cancer illnesses by using soft computing tools-neural network, genetic algorithm, evolutionary computing, chaos theory, Zadeh last theory-Z-number theory, and real-life results for giving help and advice to doctors for decision-making during the treatment process. For the future works, the data that have been used in computations will be gathered from different hospitals and centers of oncology from all over the world by using internet resources.
