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Predictive Data Mining of Chronic Diseases Using Decision Tree

A Case Study of Health Insurance Company in Indonesia

The development of information and communication technology has rapidly penetrated to several sectors including health sector. A good data management has become necessity for a healthcare company since it will provide better control of the costs and mitigate risks. However, to develop a good quality data management is complex. Therefore, data mining as one of the advancements of science and technology development offers its technique (such as decision tree) to mine the hidden information from the large amounts of medical data that may improve the decision making. It is the aim of this study 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 such as data collection, data preprocessing and data mining 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.

The development of information and communication technology has rapidly penetrated to several sectors including health sector.