Mohammadzadeh Mehdi, Hoseini Zeinab Zare, Derafshi Hamid
Department of Pharmacoeconomy&Administrative Pharmacy, Shahid Beheshti University of Medical Sciences,POBOX 14155-6153,Tehran Iran.
Department of Engineering&Technology, Payame Noor University,, Tehran, PO BOX 19395-3697, Iran.
Procedia Comput Sci. 2017;120:23-30. doi: 10.1016/j.procs.2017.11.206. Epub 2017 Dec 14.
Nowadays Health care industry has a significant growth in using data mining techniques to discover hidden information for effective decision making. Huge amount of healthcare data is suitable to mine hidden patterns and knowledge. In this paper we traced behavior of patients during the period of 3 years in three clinics of a big public sector hospital and tried to detect special groups and their tendencies by RFML model as a customer life time value (CLV). The main goal was to detect 'potential for loyal' customers for strengthen relationships and 'potential to churn' customers for recovery of the efficiency of customer retention campaigns and reduce the costs associated with churn. This strategy helps hospital administrators to increase profit and reduce costs of customers' loss. At first, K-means clustering algorithm was applied for identification of target customers and groups and then, decision tree classifier as churn prediction was used. We compared performance of three clinics based on the number of loyal and churn customers. Our results showed that Pediatric Hematology clinic had a better performance than that of other clinics, because of more number of loyal customers.
如今，医疗保健行业在使用数据挖掘技术以发现隐藏信息用于有效决策方面有显著增长。大量的医疗保健数据适合挖掘隐藏模式和知识。在本文中，我们追踪了一家大型公立医院三个诊所中患者三年期间的行为，并尝试通过作为客户终身价值（CLV）的RFML模型检测特殊群体及其趋势。主要目标是检测“忠诚潜力”客户以加强关系，以及“流失潜力”客户以恢复客户保留活动的效率并降低与客户流失相关的成本。这种策略有助于医院管理人员增加利润并降低客户流失成本。首先，应用K均值聚类算法识别目标客户和群体，然后使用决策树分类器进行客户流失预测。我们根据忠诚客户和流失客户的数量比较了三个诊所的表现。我们的结果表明，儿科血液学诊所的表现优于其他诊所，因为忠诚客户数量更多。
