Decision trees are relative known and often used intelligent tool for decision support. They are convenient for capturing the knowledge from vast amount of data. The result or decision model is represented in hierarchical manner, where the significance or contribution of single attribute to final decision is shown very clearly. When the decision tree is built upon real-world data, this data is more often numeric than discrete. While it is in human nature to better use the words than numbers in order to describe something the fuzzy logic theory was introduced to fill this gap. Description of the attribute properties using fuzzy logic approach is represented by vector of plausibility where the coordinates between 0 and 1 show the plausibility of attribute to its subsets of possible attribute values. This is the way to successfully overcome the problem of boundary values between attribute subsets where the sharp determined boundaries can incorrectly affect the result a lot. In Laboratory for system design, we have developed the software tool for building the decision trees with fuzzy heuristics function. The tool was used upon the data collected from breastfeeding booklet and the results are used for developing different advisory systems for health-care professionals as well as for breastfeeding support groups and mothers who have access to Internet.