Decision trees are commonly used for decision-making, and researchers have developed a new algorithm that optimizes decision trees using rough set theory. This algorithm reduces the number of nodes and eliminates the repetition of condition attributes, resulting in a more efficient decision tree. By using the degree of dependency in rough set theory, the algorithm selects the best attributes for splitting, improving the accuracy of the decision tree. This approach helps in classifying data more effectively and making better decisions based on the information provided.