An algorithmic context to medical pattern recognition using similarity measures of complex picture fuzzy soft set

Identifying a specific disease, especially based on common symptoms, can be challenging due to uncertainty and ambiguity, making similarity measures crucial for accurate diagnosis. Therefore, in this article, a novel structure, the similarity measures of a novel mathematical structure called complex picture fuzzy soft sets (cPFSS), is formulated. Firstly, two definitions of similarity measures for cPFSS are established: one based on the membership, non-membership, and neutral functions associated with the set, and the second one based on the distance measures of cPFSS. After that, an algorithm is suggested that is based on the proposed measures between cPFSS, and it is then applied to disease diagnosis. The computational complexity of the proposed algorithm shows that it takes a constant time, even in the case of a large dataset. Additionally, the outcomes show that one patient has the highest similarity with Tuberculosis, while another patient most closely matches Hepatitis.