Development of rule-based classification algorithms by using cuckoo optimization algorithm for diagnosing mastitis patient's disease

In recent years, successful swarm optimization algorithms have been developed, which have reduced of getting stuck to local optimums rate by searching from within the problem space through natural inspiration. The Cuckoo Bird Algorithm is a good example for this kind of algorithms. Optimization algorithms are also used for optimization in machine learning methods such as classification because they are applied on problems that are difficult to solve analytically. In this study, a rule-based classification algorithm has been developed using the Cuckoo Bird Algorithm. The algorithm developed was used for the early detection of mastitis disease in dairy cattle, which leads to large economic losses in milk production and is difficult to detect with conventional methods. The generated algorithm was implemented by using the C # .NET programming language. The software has been applied to the actual data set obtained from a computer operated dairy farm for the diagnosis of mastitis disease. By using the classification algorithm developed in the experimental results obtained, it is possible to obtain a highly successful and reliable predictor model for the early detection of mastitis disease.