A Soft Computing Decision Support System in the Diagnosis of Breast Cancer
نویسندگان
چکیده
It is well known that most of Breast cancer diagnosis characterization processes are entirely based on physician’s intuition and experience. Since diagnosis of breast cancer involves several layers of uncertainty and imprecision that makes traditional approaches inappropriate. In the present research paper a soft computing diagnostic support system for breast cancer is proposed which is capable enough to capture ambiguous and imprecise information prevalent in breast cancer diagnosis. It is user friendly and will sharpen diagnostic skill of medical practitioners.
منابع مشابه
A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer
Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy exp...
متن کاملA New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms
Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients (malignant and benign). The proposed approach, AP-AMBFA, con...
متن کاملBreast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm
Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used. First,...
متن کاملAn Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System
Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning...
متن کاملBreast Cancer Risk Assessment Using adaptive neuro-fuzzy inference system (ANFIS) and Subtractive Clustering Algorithm
Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used. First,...
متن کامل