Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 5 3 2020 06 15 183 193 Saeed Shirazi MSc, Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Hamed Baziyad MSc, Department of Information Technology, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Naser Ahmadi MSc, Department of Biostatistics, Faculty of Paramedical Science, Shahid beheshti University of medical Science, Tehran, Iran Amir Albadvi Professor, Department of Information Technology, Faculty of Industrial and Systems Engineering, Tehran, Iran 2019 08 13 2019 09 17 Background and aim: Recently, the use of data science techniques in healthcare has been increased remarkably. Community detection as one the important methods of data science is utilized in the health domain. Methods: This paper detects disease areas based on combination of big data and graph mining methods on drug prescriptions. At first, network of prescription is designed, and Louvain algorithm is applied for community detection of 50000 Iranian prescriptions in 2014 gathered from the Iranian Health Insurance Organization. We use modularity metric for validation of the results and the experts’ opinion as the external validation of communities. Results: The outputs are consist of six communities. These communities are labeled based on experts’ opinion that present the disease fields. Conclusion: The Louvain algorithm has the ability to detect the major communities of the prescription database with an acceptable accuracy. We have proven that these communities present the disease fields. https://jbe.tums.ac.ir/index.php/jbe/article/view/292 https://jbe.tums.ac.ir/index.php/jbe/article/download/292/227