Data mining applications in healthcare businesses

This thesis examines the role of data mining techniques in improving the performance of healthcare businesses. Data mining is defined as the process of obtaining meaningful information from large and complex data sets and carries great potential for the healthcare industry. Healthcare businesses can improve service quality and operational efficiency by analyzing patient information and discovering connections between seemingly unrelated data.The main purpose of this study is to reduce patient density in healthcare institutions and minimize time loss in outpatient clinic referrals by using data mining techniques. In the study, a literature review was conducted on the use of data mining in the healthcare sector, frequently used data mining techniques, especially the Apriori algorithm, were examined and how these techniques could be applied in healthcare businesses was investigated. In this regard, it has been determined that data mining techniques provide various advantages in healthcare businesses. These advantages include increasing patient satisfaction, reducing operational costs and improving the quality of healthcare services. In particular, data mining techniques based on the use of association rules have been shown to be effective in determining the connection rules between the services applied by patients. It is thought that the effective use of data mining techniques in healthcare businesses will improve the decision-making processes of businesses and increase patient satisfaction. It is recommended that future research deepen knowledge in this area by using larger data sets and different data mining algorithms.