Mining Association Rules of Breast Cancer Based on Fuzzy Rough Set

Abstract

In view of the current increasing status of breast cancer patients, to enable patients to predict whether they have breast cancer by themselves from physical examination data, this paper proposes a method for mining breast cancer association rules based on fuzzy rough sets. The method proposed in this paper first analyzes the attributes in the traditional blood data, then applies the attribute reduction of the fuzzy rough set, deletes the attributes irrelevant to breast cancer, and uses the Apriori algorithm in data mining to obtain the frequent items in the remaining attributes Set, apply low support and high confidence to extract many practical, strong association rules. Specific examples verify this method. The experimental results show that this method can dig out more and higher-quality rules compared with traditional algorithms. At the same time, these extracted rules are highly effective reference values in diagnosing and preventing breast cancer.

Blockchain integrated multi-agent system for breast cancer diagnosis

“…The artificial intelligence techniques can give a reliable and faster approach for detecting breast cancer in patients [34], [35]. The artificial intelligence techniques are used in prediction of breast cancer either from the medical images [36]- [43] or from the datasets containing categorical and numerical values [44], [45]. The deep learning approaches are most commonly used for detection of breast cancer from medical images [46], [47].…”