A New Application of Louvain Algorithm for Identifying Disease Fields Using Big Data Techniques
Abstract
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.
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41. Szlezák N, Evers M, Wang J, Pérez L. The Role of Big Data and Advanced Analytics in Drug Discovery , Development , and Commercialization. 2014;95(5):3-6. doi:10.1038/clpt.2014.29.
42. Udrescu L, Sbârcea L, Topîrceanu A, et al. Clustering drug-drug interaction networks with energy model layouts: Community analysis and drug repurposing. Sci Rep. 2016;6(June):1-10. doi:10.1038/srep32745.
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44. Brandes U, Borgatti SP, Freeman LC. Maintaining the duality of closeness and betweenness centrality ଝ. Soc
Networks. 2016;44:153-159. doi:10.1016/j.socnet.2015.08.003 .
45. Que X, Checconi F, Gunnels JA. Scalable Community Detection with the Louvain Algorithm. 2015. doi:10.1109/IPDPS.2015.59.
46. Mastering Spark for Data Science - Andrew Morgan, Antoine Amend, David George, Matthew Hallett - Google Books.
2. Suseela BBJ, Jeyakrishnan V. A MULTIOBJECTIVE HYBRID ACO-PSO OPTIMIZATION ALGORITHM FOR VIRTUAL MACHINE PLACEMENT IN CLOUD COMPUTING. 2014:23192322.
3. Herland M, Khoshgoftaar TM, Wald R. Open Access A review of data mining using big data in health informatics. 2014.
4. Manogaran G, Thota C, Lopez D, Vijayakumar V, Abbas KM, Sundarsekar R. Big Data Knowledge System in Healthcare. doi:10.1007/978-3-319-49736-5.
5. Denny JC. Chapter 13 : Mining Electronic Health Records in the Genomics Era. 2012;8(12).
doi:10.1371/journal.pcbi.1002823
6. Fortunato S. Community detection in graphs. Phys Rep. 2010;486(3-5):75-174. doi:10.1016/j.physrep.2009.11.002
7. Newman MEJ. Modularity and community structure in networks. 2006;103(23):8577-8582.
8. Wang M, Wang C, Yu JX, Zhang J. Community Detection in Social Networks : An In-depth Benchmarking Study with a Procedure-Oriented Framework. :998-1009.
9. Li Z, Liu J. A multi-agent genetic algorithm for community detection in complex networks. 2016;449:336347. doi:10.1016/j.physa.2015.12.126.
10. He T, Meng T, Chen L, Deng Z, Cao Z. Parallel Community Detection Based on Distance Dynamics For Large-scale. IEEE Access. 2018;PP(c):1. doi:10.1109/ACCESS.2018.2859788.
11. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. 2008:1-12. doi:10.1088/17425468/2008/10/P10008.
12. Dabas C, Nagar H, Kumar G. ScienceDirect ScienceDirect Large Scale Graph Evaluation for Find Communities in Big Data Large Scale Graph Evaluation for Find Communities in Big Data. Procedia Comput Sci. 2018;132:263-270. doi:10.1016/j.procs.2018.05.171.
13. Chopade P, Zhan J. Community Detection in Large Scale Big Data Networks. (1137443).
14. Chopade P, Zhan J. Networks Using GameTheoretic Modeling. 2016;XX(X). doi:10.1109/TBDATA.2016.2628725
15. Long H. PT US CR. Inf Sci (Ny). 2018. doi:10.1016/j.ins.2018.03.063.
16. Pirouz M, Zhan J. Optimized Label Propagation Community Detection on Big Data Networks. 2018.
17. Ahajjam S, Haddad M El, Badir H. A new scalable leader-community detection approach for community detection in social networks. Soc Networks. 2018;54:41-49. doi:10.1016/j.socnet.2017.11.004.
18. Karyotis V, Tsitseklis K, Sotiropoulos K. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications. :1-21.doi:10.3390/s18041205.
19. Li X, Cao X, Qiu X, Zhao J, Zheng J. Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark. Proc - 5th Int Conf Adv Cloud Big Data, CBD 2017. 2017:176-181. doi:10.1109/CBD.2017.38.
20. Bichot, C.-E. and P. Siarry G partitioning. 2013: JW& S. No Title.
21. Hung S, Araujo M, Faloutsos C. Distributed Community Detection on Edge-labeled Graphs using Spark.
doi:10.1145/1235.22. Guendouz M, Amine A, Mohamed R. A discrete modified fireworks algorithm for community.
2017:373385. doi:10.1007/s10489-016-0840-9.
23. Moon S, Lee J, Kang M. Scalable Community Detection from Networks by Computing Edge Betweenness on MapReduce. 2014:14-17.
24. Ovelg M. Distributed Community Detection in Web-Scale Networks. 2013:66-73.
25. Saltz M, Prat-pérez A, Dominguez-sal D. Distributed Community Detection with the WCC Metric. :1095-1100.
26. Sharma R, Oliveira S. Community Detection Algorithm for Big Social Networks Using Hybrid Architecture. Big Data Res. 2017;10:44-52. doi:10.1016/j.bdr.2017.10.003.
27. Ciprian-Octavian Truic˘a, Olivera Novovi´c,Sanja Brdar ANP. No Title. In: Community Detection in Who-Calls-Whom Social Networks. Conference: International Conference on Big Data Analytics and Knowledge Discovery; 2018:15.
28. Landon BE, Onnela JP, Keating NL, et al. Using administrative data to identify naturally occurring networks of physicians. Med Care. 2013;51(8):715-721. doi:10.1097/MLR.0b013e3182977991.
29. Cox M, Ellsworth D. Application-Controlled Demand Paging for Out-of-Core Visualization Page size.
30. Bryant RE, Katz RH, Lazowska ED. Big-Data Computing : Creating revolutionary breakthroughs in commerce , science , and society Motivation : Our DataDriven World. 2008.
31. Sood SK, Sandhu R, Singla K, Chang V. Sustainable Computing : Informatics and Systems IoT , big data and HPC based smart flood management framework. Sustain Comput Informatics Syst. 2018;20:102-117.
oi:10.1016/j.suscom.2017.12.001
32. Kulennavar PN. A Survey On Big Data Analytics In Health Care. 2014;5(4):5865-5868.
33. Big data : The next frontier for innovation , competition , and productivity. 2011;(June).
34. Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K, Kumar A. A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Futur Gener Comput Syst. 2018. doi:10.1016/j.future.2018.03.005.
35. Swan M. THE QUANTIFIED SELF : 2013;1(2):85-99. doi:10.1089/big.2012.0002.
36. Krumholz HM. Downloaded from content.healthaffairs.org by Health Affairs on April 5, 2015 at UNIV OF MASSACHUSETTS. 2014. doi:10.1377/hlthaff.2014.0053.
37. Taher A, Aboul A, Hassanien E. Dimensionality reduction of medical big data using neural-fuzzy classifier. 2014. doi:10.1007/s00500-014-1327-4.
38. Yao Q, Tian Y, Li P, Tian L. Design and Development of a Medical Big Data Processing System Based on Hadoop. 2015. doi:10.1007/s10916-015-0220-8.
39. Shaikh AR, Butte AJ, Schully SD, Dalton WS, Khoury J, Hesse BW. Collaborative Biomedicine in the Age of Big Data : The Case of Cancer Corresponding Author : 2014;16:1-5. doi:10.2196/jmir.2496.
40. Meyer A, Olshan AF, Green L, et al. Big Data for Population-Based Cancer Research : 2014;75(4):265269.
41. Szlezák N, Evers M, Wang J, Pérez L. The Role of Big Data and Advanced Analytics in Drug Discovery , Development , and Commercialization. 2014;95(5):3-6. doi:10.1038/clpt.2014.29.
42. Udrescu L, Sbârcea L, Topîrceanu A, et al. Clustering drug-drug interaction networks with energy model layouts: Community analysis and drug repurposing. Sci Rep. 2016;6(June):1-10. doi:10.1038/srep32745.
43. Chautard E, Thierry-mieg N, Ricard-blum S. Interaction networks : From protein functions to drug discovery . A review seaux d ’ interactions : de la fonction des prote Les re ` la conception de me ´ dicaments . Une revue a. 2009;57:324-333. doi:10.1016/j.patbio.2008.10.004.
44. Brandes U, Borgatti SP, Freeman LC. Maintaining the duality of closeness and betweenness centrality ଝ. Soc
Networks. 2016;44:153-159. doi:10.1016/j.socnet.2015.08.003 .
45. Que X, Checconi F, Gunnels JA. Scalable Community Detection with the Louvain Algorithm. 2015. doi:10.1109/IPDPS.2015.59.
46. Mastering Spark for Data Science - Andrew Morgan, Antoine Amend, David George, Matthew Hallett - Google Books.
| Files | ||
| Issue | Vol 5 No 3 (2019) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v5i3.3613 | |
| Keywords | ||
| Big Data Graph theory Community Detection Drug Prescription | ||
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How to Cite
1.
Shirazi S, Baziyad H, Ahmadi N, Albadvi A. A New Application of Louvain Algorithm for Identifying Disease Fields Using Big Data Techniques. JBE. 2020;5(3):183-193.
