Predictive data mining of chronic diseases using decision tree: A case study of health insurance company in Indonesia

• Main Authors: Qudsi, Dini Hidayatul, Kartiwi, Mira, Saleh, Nurliyana
• Format: Article
• Language: English; English
• Published: Research India Publication 2017
• Subjects: H Social Sciences (General); T Technology (General); T10.5 Communication of technical information; T58.5 Information technology
• Online Access: http://irep.iium.edu.my/57292/1/ijaerv12n7_34.pdf; http://irep.iium.edu.my/57292/2/57292-Predictive%20Data%20Mining%20of%20Chronic%20Diseases%20Using%20Decision%20Tree_SCOPUS.pdf; http://irep.iium.edu.my/57292/; https://www.ripublication.com/ijaer17/ijaerv12n7_34.pdf

This study aims to identify the potential benefits that data mining can bring to the health sector, using Indonesian Health Insurance company data as case study. The most commonly data mining technique, decision tree, was used to generate the prediction model by visualizing the tree to perform predictive analysis of chronic diseases. All the steps in data mining process have been performed by a data mining tool, named WEKA. Additionally, WEKA also was utilized to evaluate the prediction performance by measuring the accuracy, the specificity and the sensitivity. Among the result found in this study shows some factors that the health insurance can take into account when predicting the treatment cost of a patient.