Decision tree using ant colony for classification of health data

The classification algorithm's goal is to built a model that maximizes the accuracy of the number of correct predictions, although the completeness of the model plays an important role in many application areas. Ant Colony Optimization (ACO) is relatively simple to realize the behavior of ant colonies, and they cooperate with each other to achieve the goal from nest to food source. A system capable of executing a search to discover the optimum answer to an optimization issue with a vast search space is referred to as a colony generation system. Classification by applying the ACO algorithm in data mining has the advantage of searching with flexible values and value combinations. One of the many benefits that can be applied using ACO is to build a decision tree. As a model representation, the decision tree is easy to understand and can be represented in the form of a graph. By using the modified decision tree using ACO, the result of using the pruning technique is 76.1%.