medigraphic.com
ISSN 2307-2113 (Electronic)
Revista Cubana de Información en Ciencias de la Salud (ACIMED)

2013, Number 4

<< Back Next >>

Revista Cubana de Información en Ciencias de la Salud (ACIMED) 2013; 24 (4)

Spatial data mining and its application in health and epidemiology studies

González PL, Pérez BYG

Language: Spanish
References: 24
Page: 482-489
PDF size: 53.10 Kb.


ABSTRACT

The accumulation of spatial data resulting from the development of information systems, especially geographic information systems, has paved the way for the application of spatial data mining techniques for the extraction of new knowledge which could in turn assist in decision making. Health and epidemiology areas have not been alien to the development and use of these systems, revalidating the importance of the spatial component both in research and in the design of differentiated prevention and control strategies for each health area. The paper presents the methodological aspects and concepts associated with spatial data mining. A description is provided of the main algorithms and tools used in spatial data mining, and some experiences are presented which illustrate the application trends and potential of this technique in health and epidemiology areas.


REFERENCES

  1. Alegret Rodríguez M, Herrera M, Grau Abalo R. Las técnicas de estadística espacial en la investigación salubrista: caso síndrome de Down. Rev Cubana Sal Públ [revista en la Internet]. 2008 [citado 13 de agosto de 2013];34(4). Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-34662008000400003&lng=es

  2. Barcellos C, Buzai GD. La dimensión espacial de las desigualdades sociales en salud: aspectos de su evolución conceptual y metodológica. Departamento de Ciencias Sociales. Universidad Nacional de Luján: Anuario de la División Geografía; 2006:275-92.

  3. Más Bermejo P. Desarrollo, tendencia actual y retos de la Epidemiología en Cuba. Rev Cubana Med Trop. 2011;63:5-6.

  4. Orallo JH, Quintana MJR, Ramírez CF. Introducción a la minería de datos: Pearson Prentice Hall; 2004.

  5. McDonnell R, de la Fuente Aragón M, McDonnell R, editors. Minería de datos aplicada a la gestión de la información urbanística. Data mining applied to urban information management. 6th International Conference on Industrial Engineering and Industrial Management; 2012.

  6. Rigol-Sánchez JP, Chica-Olmo M, Pardo-Igúzquiza E, Rodríguez-Galiano V, Chica-Rivas M. Análisis e integración de datos espaciales en investigación de recursos geológicos mediante sistemas de información geográfica. Bol Soc Geol Mex. 2011;63(1):61-70.

  7. Cangrejo Aljure D, Agudelo JG. Minería de datos espaciales Spatial data miningAn overview. Rev Avanc Sist Informát. 2011;8(3):71-7.

  8. Dueñas Reyes MX. Minería de datos espaciales en búsqueda de la verdadera información. Ing Univ. 2009:137-56.

  9. Han J, Kamber M. Data mining: concepts and techniques. Morgan Kaufmann; 2006.

  10. Ester M, Kriegel HP, Sander J. Knowledge discovery in spatial databases. KI-99. Advanc Artif Intellig. 1999:696.

  11. Ng RT, Han J. Clarans: a method for clustering objects for spatial data mining. Knowledge and Data Engineering. IEEE Transactions on. 2002;14(5):1003-16.

  12. Celik M, Dadaser Celik F, Dokuz A, editors. Anomaly detection in temperature data using DBSCAN algorithm. Innovations in Intelligent Systems and Applications (INISTA). International Symposium. IEEE; 2011.

  13. Ester M, Kriegel HP, Sander J. Algorithms and applications for spatial data mining. Geographic Data Mining and Knowledge Discovery. 2001.

  14. Olman V, Mao F, Wu H, Xu Y. Parallel clustering algorithm for large data sets with applications in bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2009;6(2):344-52.

  15. Danalis A, McCurdy C, Vetter JS. Efficient Quality Threshold Clustering for Parallel Architectures. Parallel & Distributed Processing Symposium (IPDPS) IEEE 26th International; 2012: 1068-79.

  16. Xu X, Jäger J, Kriegel HP. A fast parallel clustering algorithm for large spatial databases. High Perform Dat Min. 2002:263-90.

  17. Kux HJ, Souza UD. Object-based image analysis of WorldView-2 satellite data for the classification of mangrove areas in city of São Luís. Brazil: An Photogr, Rem Sens Spat Inform Sc. 2012.

  18. Korting TS, Fonseca LMG, Escada MIS, da Silva FC, dos Santos Silva MP. GeoDMA - A novel system for spatial data mining. Data Mining Workshops. IEEE International Conference; 2008.

  19. Bae DH, Baek JH, Oh HK, Song JW, Kim SW. SD-Miner: A spatial data mining system. Network Infrastructure and Digital Content. IEEE International Conference; 2009.

  20. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Statistics in medicine. 1995;14(8):799-810.

  21. Coleman M, Mabuza AM, Kok G, Coetzee M, Durrheim DN. Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malar J. 2009;8:68.

  22. Vinnakota S, Lam NS. Socioeconomic inequality of cancer mortality in the United States: a spatial data mining approach. Internat J Heal Geogr. 2006;5(1):9.

  23. Zhao F, Zhu R, Zhang L, Zhang Z, Li Y, He M, et al. Application of satscan in detection of schistosomiasis clusters in marshland and lake areas. Zhongguo xue xi chong bing fang zhi za zhi. Chin J Schistosom Contr. 2011;23(1):28.

  24. Hernández NEB, Rodríguez MA, Fleites OA. Análisis espacial de la morbimortalidad del cáncer de mama y cérvix. Villa Clara. Cuba. 2004-2009. Rev Esp Sal Públ. 2013;87:49-57.




2020     |     www.medigraphic.com