a fuzzy expert system for diagnosis of acute lymphocytic leukemia in children

fuzzy expert systems are one of the most practical intelligent models with the high potential for managing uncertainty associated to the medical diagnosis. in this paper, a fuzzy inference system (fis) for diagnosing of acute lymphocytic leukemia in children has been introduced. the fuzzy expert system applies mamdani reasoning model that has high interpretability to explain system results to experts in a high level. the system has been designed based on the specialist physician’s knowledge. the proposed systems, has been implemented in matlab and evaluated on real patients’ dataset. high accuracy of this system (with an accuracy about 96%) revealed its capability for helping experts to early diagnosis of the disease. that the results are promising for more earlier diagnosis and then providing good treatment of patients and consequently saving more children’s lives.