The notion of fuzzy N-soft sets is a hybrid model, which is a more generalized framework than fuzzy soft sets. To investigate the objects of a reference set in medical field, which have uncertainties in data, can be correctly captured by proposed structures of novel decision-making methods, graded TOPSIS and graded ELECTRE-I methods, based on fuzzy N-soft sets (henceforth, (F, N)-soft sets). Both the proposed methods compute the decision-maker estimations in a more flexile and affluent way, as well as improve the reliability of the decisions, that depends on star ratings or grades for the purpose of the modelization of decision-making problems in medical field. We show the importance and feasibility of proposed methods by applying them on real life example in medical field having ambiguities, that can be accurately occupied by this framework. Finally, we discuss the comparison analysis of both the proposed decision-making methods.
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