Comparison of the performance of data mining algorithms on the endocrine data set

With the increase in developing technology facilities, data can be stored in many areas. Data analysis methods and solutions are needed to use it for meaningful, interpreted, and used for the benefit of humanity by way of the data obtained. With the advancement of technology, large and complex databases are formed in the developing medical. With data mining methods, detecting meaningful data from these complex databases, creating an infrastructure, detecting the problem, solving the problem, or providing a faster and various perspective in the diagnosis of a disease. In this study, the patient's profile was tried to be determined by considering the data set containing the blood test information of the patients who applied to the Internal Diseases Policlinic of Pamukkale University Hospital. A person who is known to have one of the diseases worked on the relationship between the other three diseases studied and the relationship between these four diseases is thought to be used in the preliminary diagnosis of future disorders. In addition, the performance of Apriori, ECLAT, FP-Tree, and H-Mine algorithms used in the study have been examined on the dataset and their performance differences have been evaluated against each other