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The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human\u2010understandable terms\u2010linguistic variables. The Mamdani Fuzzy Rule\u2010Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels\u2010 benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state\u2010of\u2010the\u2010art dataset.<\/jats:p>","DOI":"10.1049\/ipr2.12774","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:54:25Z","timestamp":1678679665000},"page":"2083-2096","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A fuzzy rule\u2010based system with decision tree for breast cancer detection"],"prefix":"10.1049","volume":"17","author":[{"given":"Vedika","family":"Gupta","sequence":"first","affiliation":[{"name":"Jindal Global Business School O.P. 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