Predicting the chances of various types of cancers for different organs in the human body is a typical decision-making process in medicine and health. The signaling pathways have played a vital role in increasing or decreasing the possibility of the deadliest disease, cancer. To combine the pathways concept and ambiguity in the prediction techniques of such diseases, we have used the proposed research on fuzzy graphoidal covers of fuzzy graphs in this paper. Determining a path with uncertainty and shortest length is a challenging topic of graph theory, and a collection of such shortest paths maintaining specific conditions is defined as a fuzzy graphoidal cover for a fuzzy graph. Also, we have defined fuzzy graphoidal covering number as a new parameter, reflecting the measure of coverage by fuzzy graphoidal covering set in a system. Afterwards, some important characterizations of the fuzzy graphoidal covering number are established with justified proof. Also, specific limit values of this number are provided for particular cases. Then, we developed an efficient algorithm for finding the defined covering set with its space and time complexity. The findings of this proposed study have been composed with an artificial neural network to model a strong tool for resolving an essential issue of medical sciences, the prediction of cancer type in the human body. We have analyzed two types of neural networks such as one one-dimensional and two-dimensional specification, for clarity of the obtained results. Also, we have found out the most possible cancer type is breast cancer from the data of our considered case study as a concluding statement for any decision-maker in the field of health sciences. Finally, sensitivity analysis and comparative study have been done to show the stability of our proposed work.
Keywords: Cancer type prediction; Covering problem; Fuzzy graph; Fuzzy graphoidal covering number; Fuzzy shortest path.
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