فهرست مطالب leila honari mahmod

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.

Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligentModel based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order.