# Medical diagnoses using three-way fuzzy concept lattice and their Euclidean distance

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## Abstract

Searching the closest symptoms of a particular disease in the given patient is a major concern for medical diagnoses expert. In most cases, this problem arises due to large number of incomplete, uncertain, or inconsistent information generated in medical diagnoses data set. Recently, some of the researchers tried to pay attention towards precise measurement of uncertainty and vagueness in medical data set using the properties of applied abstract algebra. In this process, researchers noticed that calculus of three-way decision space is more useful to characterize the uncertainty and vagueness in medical data set based on truth, indeterminacy, and falsity membership-values. However, searching some relevant query based on user requirement to diagnoses the disease is a major issue in three-way decision space. To encounter this problem, current paper tried to analyze the medical data set using the properties of single-valued neutrosophic graph-based concept lattice. In addition, another method is proposed to select some of the interesting three-way fuzzy concepts at user-defined granulation for their computed Euclidean distance with an illustrative example.

## Keywords

Formal concept analysis Fuzzy concept lattice Medical diagnoses Neutrosophic set Three-way fuzzy concept lattice## Mathematics Subject Classification

68Txx (Artificial intelligence) 68Rxx (Discrete mathematics in relation to computer science) 06Bxx (Lattices)## Notes

### Acknowledgements

The Author sincerely thanks the anonymous reviewers for their valuable suggestions to improve the quality of this paper.

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