Algorithms for constructing hesitant neutrosophic concept lattices and computing their similarity distances in medical decision-making

In medical decision-making, pros and cons of balancing treatment options are often required. However, clinical experts may well put forward their opinions on the basis of huge uncertain or incomplete information. To address this issue, the notion of hesitant neutrosophic concept lattice (HNCL) has been utilized to express the decision information based on hesitant neutrosophic sets (HNS) and formal concept analysis (FCA). Here, we present another improved hybrid model to measure the similarity distance among hesitant neutrosophic concepts (HNCon). An actual example was used to demonstrate the effectiveness and reliability of the proposed method. Category: Bioinformatics. © 2019, The Korean Institute of Information Scientists and Engineers.