Open AccessA New Application of Louvain Algorithm for Identifying Disease Fields Using Big Data Techniques
Author(s) -
Saeed Shirazi,
Hamed Baziyad,
Naser Ahmadi,
Amir Albadvi
Publication year - 2020
Publication title -
journal of biostatistics and epidemiology
Language(s) - English
Resource type - Journals
eISSN - 2383-420X
pISSN - 2383-4196
DOI - 10.18502/jbe.v5i3.3613
Subject(s) - medical prescription , modularity (biology) , computer science , big data , metric (unit) , data mining , data science , domain (mathematical analysis) , disease , health care , graph , medicine , theoretical computer science , mathematics , engineering , mathematical analysis , operations management , genetics , pathology , economics , pharmacology , biology , economic growth
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.