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A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia

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Computer Information Systems and Industrial Management (CISIM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12133))

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Abstract

There is a huge problem in public health around the world called severe maternal morbidity (SMM). It occurs during pregnancy, delivery, or puerperium. This condition establishes risk for babies and women lives since it’s earlier detection isn’t easy [8]. In order to respond to such a situation, the current study suggests the use of logistic regression, and supports vector machine to construct a predicting model of risk level of maternal morbidity during pregnancy. Patients for the current study was the pregnant women who received prenatal care at Rafael Calvo Clinic in Cartagena, Colombia and final attention in the same clinic. This study presents the results of two machine learning algorithms, logistic regression and support vector machine. We validated the datasets from the first, second and third quarter of pregnancy with both techniques. The study shows that logistic regression achieves the best results with the prenatal control dataset from the first and second quarter and the support vector machine algorithm achieves the best prediction results with the data set from the third quarter. We generated two datasets using the information of medical records on pregnancy patients at Maternidad Rafael Calvo Clinic. The first dataset contains the six initial months of pregnancy data and the second dataset contains the last quarter of pregnancy data. We trained the first model with logistic regression and the datasets corresponding to the first semester of pregnancy. We obtained a classification of 97% sensibility, 51.8% positive predictive value and F1 score of 67.7%. The support vector machine model was implemented with the datasets obtained from the third quarter of pregnancy. We obtained a classifier with 100% of sensibility, 27.0% of precision.

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References

  1. Caicedo-Torres, W., Paternina, Á., Pinzón, H.: Machine learning models for early dengue severity prediction. In: Montes-y-Gómez, M., Escalante, H.J., Segura, A., Murillo, J.D. (eds.) IBERAMIA 2016. LNCS (LNAI), vol. 10022, pp. 247–258. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47955-2_21

    Chapter  Google Scholar 

  2. Calvert, J.S., et al.: A computational approach to early sepsis detection. Comput. Biol. Med. 74, 69–73 (2016)

    Article  Google Scholar 

  3. Farran, B., Channanath, A.M., Behbehani, K., Thanaraj, T.A.: Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait—a cohort study. BMJ Open 3(5), e002457 (2013)

    Article  Google Scholar 

  4. Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Mach. Learn. 31(1), 1–38 (2004)

    MathSciNet  Google Scholar 

  5. Feizi-Derakhshi, M.R., Ghaemi, M.: Classifying different feature selection algorithms based on the search strategies. In: International Conference on Machine Learning, Electrical and Mechanical Engineering (2014)

    Google Scholar 

  6. Haaga, J.G., Wasserheit, J.N., Tsui, A.O., et al.: Reproductive Health in Developing Countries: Expanding Dimensions, Building Solutions. National Academies Press, Washington, D.C. (1997)

    Google Scholar 

  7. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  8. Jahan, S., Begum, K., Shaheen, N., Khandokar, M.: Near-miss/severe acute maternal morbidity (SAMM): a new concept in maternal care. J. Bangladesh Coll. Phys. Surg. 24(1), 29–33 (2006)

    Article  Google Scholar 

  9. Lorduy Gómez, J., Carrillo González, S., Muñoz Baldiris, R.E., Díaz-Pérez, A., Perez, I.: Prognostic factors of early neonatal sepsis in the city of Cartagena Colombia (2018)

    Google Scholar 

  10. Mani, S., et al.: Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J. Am. Med. Inform. Assoc. 21(2), 326–336 (2014)

    Article  Google Scholar 

  11. Nanda, S., Savvidou, M., Syngelaki, A., Akolekar, R., Nicolaides, K.H.: Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks. Prenat. Diagn. 31(2), 135–141 (2011)

    Article  Google Scholar 

  12. Ng, A.: Machine learning: Stanford machine learning course materials

    Google Scholar 

  13. Nilashi, M., bin Ibrahim, O., Ahmadi, H., Shahmoradi, L.: An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. 106, 212–223 (2017)

    Google Scholar 

  14. World Health Organization, UNICEF: Revised 1990 estimates of maternal mortality: a new approach. World Health Organization (1996)

    Google Scholar 

  15. Park, F.J., Leung, C.H., Poon, L.C., Williams, P.F., Rothwell, S.J., Hyett, J.A.: Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy. Aust. N. Z. J. Obstet. Gynaecol. 53(6), 532–539 (2013)

    Article  Google Scholar 

  16. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  17. Poon, L.C., Kametas, N.A., Maiz, N., Akolekar, R., Nicolaides, K.H.: First-trimester prediction of hypertensive disorders in pregnancy. Hypertension 53(5), 812–818 (2009)

    Article  Google Scholar 

  18. Rodríguez, E.A., Estrada, F.E., Torres, W.C., Santos, J.C.M.: Early prediction of severe maternal morbidity using machine learning techniques. In: Montes-y-Gómez, M., Escalante, H.J., Segura, A., Murillo, J.D. (eds.) IBERAMIA 2016. LNCS (LNAI), vol. 10022, pp. 259–270. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47955-2_22

    Chapter  Google Scholar 

  19. Tsui, A.O., Wasserheit, J.N., Haaga, J.G., et al.: Healthy pregnancy and childbearing (1997)

    Google Scholar 

  20. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  21. Yang, Z., Zhang, T., Lu, J., Zhang, D., Kalui, D.: Optimizing area under the ROC curve via extreme learning machines. Knowl.-Based Syst. 130, 74–89 (2017)

    Article  Google Scholar 

  22. Zheng, Z., Li, Y., Cai, Y.: The logistic regression analysis on risk factors of hypertension among peasants in east china & its results validating. Int. J. Comput. Sci. Issues (IJCSI) 10(2 Part 1), 416 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad del Sinú Cartagena Elias Bechara Zainum, Cartagena, Colombia

    Eugenia Arrieta Rodríguez

  2. Department of Computer Science, Oviedo University, Oviedo, Spain

    Fernando López-Martínez

  3. Universidad Tecnológica de Bolívar, Cartagena, Colombia

    Juan Carlos Martínez Santos

Authors
  1. Eugenia Arrieta Rodríguez
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  2. Fernando López-Martínez
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  3. Juan Carlos Martínez Santos
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Corresponding author

Correspondence to Eugenia Arrieta Rodríguez .

Editor information

Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

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Arrieta Rodríguez, E., López-Martínez, F., Martínez Santos, J.C. (2020). A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2020. Lecture Notes in Computer Science(), vol 12133. Springer, Cham. https://doi.org/10.1007/978-3-030-47679-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-47679-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47678-6

  • Online ISBN: 978-3-030-47679-3

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