Predicting the Risk Associated to Pregnancy using Data Mining

Andreia Brandão, Eliana Pereira, Filipe Portela, Manuel Santos, António Abelha, José Machado

2015

Abstract

Woman willing to terminate pregnancy should in general use a specialized health unit, as it is the case of Maternidade Júlio Dinis in Porto, Portugal. One of the four stages comprising the process is evaluation. The purpose of this article is to evaluate the process of Voluntary Termination of Pregnancy and, consequently, identify the risk associated to the patients. Data Mining (DM) models were induced to predict the risk in a real environment. Three different techniques were considered: Decision Tree (DT), Support Vector Machine (SVM) and Generalized Linear Models (GLM) to perform the classification task. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied to drive this work. Very promising results were obtained, achieving a sensitivity of approximately 93%.

References

  1. Abelha, A., Analide, C., Machado, J., Neves, J., & Novais, P. (2007). Ambient Intelligence and Simulation in Health Care Virtual Scenarios, 243, 461-468.
  2. Bonney, W. (2013). Applicability of Business Intelligence in Electronic Health Record. Procedia - Social and Behavioral Sciences, 73, 257-262. doi:10.1016/j.sbspro.2013.02.050.
  3. Catley, C., Frize, M., Walker, C. R., & Petriu, D. C. (2006). Predicting High-Risk Preterm Birth Using Artificial Neural Networks. IEEE Transactions on Information Technology in Biomedicine, 10(3), 540- 549. doi:10.1109/TITB.2006.872069.
  4. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). The CRISP-DM User Guide. NCR Systems Engineering Compenhagen. Brussels: NCR Systems Engineering Copenhagen.
  5. Chattopadhyay, S., Ray, P., Chen, H. S., Lee, M. B., & Chiang, H. C. (2008). Suicidal Risk Evaluation Using a Similarity-Based Classifier. In C. Tang, C. Ling, X. Zhou, N. Cercone, & X. Li (Eds.), Advanced Data Mining and Applications SE - 7 (Vol. 5139, pp. 51- 61). Springer Berlin Heidelberg. doi:10.1007/978-3- 540-88192-6_7.
  6. Cios, K., Pedrycz, W., Swiniarski, R., & Kurgan, L. (2007). Data Mining. A knowledge Discovery Approach. Springer.
  7. Goebel, R., Siekmann, J., & Wahlster, W. (2008). Advances in Knowledge Discovery and Data Mining. Springer.
  8. Gonçalves, J., Portela, F., Santos, M. F., Silva, Á., Machado, J., Abelha, A., & Rua, F. (2013). Real-time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine. International Information Institute.
  9. O'Sullivan, D., Elazmeh, W., Wilk, S., Farion, K., Matwin, S., Michalowski, W., & Sehatkar, M. (2008). Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data. In Z. Ras, S. Tsumoto, & D. Zighed (Eds.), Mining Complex Data SE - 19 (Vol. 4944, pp. 238-251). Springer Berlin Heidelberg. doi:10.1007/978-3-540- 68416-9_19.
  10. Palaniappan, S., & Awang, R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques. In Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications (pp. 108-115). Washington, DC, USA: IEEE Computer Society. doi:10.1109/AICCSA.2008.4493524.
  11. Peixoto, H., Santos, M., Abelha, A., & Machado, J. (2012). Intelligence in Interoperability with AIDA. In L. Chen, A. Felfernig, J. Liu, & Z. Ras (Eds.), Lecture Notes in Computer Science, Foundations of Intelligent Systems - 31 (Vol. 7661, pp. 264-273). Springer Berlin Heidelberg. doi:10.1007/978-3-642-34624- 8_31.
  12. Pinto, L. (2009). Sistemas de informação e profissionais de enfermagem. Universidade de Trás-Os-Montes e Alto Douro.
  13. Razavi, A., Gill, H., Åhlfeldt, H., & Shahsavar, N. (2007). Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts. Journal of Medical Systems, 31(4), 263-273. doi:10.1007/s10916-007-9064-1.
  14. Valente, C., Cristina, T., Rosário, F., & Alcina, B. (2012). Acompanhamento de enfermagem na interrupção da gravidez por opção da mulher ( I.G.O.). Porto.
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Paper Citation


in Harvard Style

Brandão A., Pereira E., Portela F., Santos M., Abelha A. and Machado J. (2015). Predicting the Risk Associated to Pregnancy using Data Mining . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 594-601. DOI: 10.5220/0005286805940601


in Bibtex Style

@conference{icaart15,
author={Andreia Brandão and Eliana Pereira and Filipe Portela and Manuel Santos and António Abelha and José Machado},
title={Predicting the Risk Associated to Pregnancy using Data Mining},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={594-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005286805940601},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Predicting the Risk Associated to Pregnancy using Data Mining
SN - 978-989-758-074-1
AU - Brandão A.
AU - Pereira E.
AU - Portela F.
AU - Santos M.
AU - Abelha A.
AU - Machado J.
PY - 2015
SP - 594
EP - 601
DO - 10.5220/0005286805940601