A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia
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...
- Autores:
-
Arrieta Rodríguez, Eugenia
López-Martínez, Fernando
Martínez Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9379
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9379
- Palabra clave:
- Severe maternal morbidity
Machine learning
Logistic regression
Support Vector Machine
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| title |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| spellingShingle |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia Severe maternal morbidity Machine learning Logistic regression Support Vector Machine |
| title_short |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| title_full |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| title_fullStr |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| title_full_unstemmed |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| title_sort |
A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombia |
| dc.creator.fl_str_mv |
Arrieta Rodríguez, Eugenia López-Martínez, Fernando Martínez Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Arrieta Rodríguez, Eugenia López-Martínez, Fernando Martínez Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Severe maternal morbidity Machine learning Logistic regression Support Vector Machine |
| topic |
Severe maternal morbidity Machine learning Logistic regression Support Vector Machine |
| description |
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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2020 |
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2020-09-10T21:23:04Z |
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2020-09-10T21:23:04Z |
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2020-05-22 |
| dc.date.submitted.none.fl_str_mv |
2020-09-07 |
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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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978-3-030-47679-3 |
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https://hdl.handle.net/20.500.12585/9379 |
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10.1007/978-3-030-47679-3_18 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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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_18 978-3-030-47679-3 10.1007/978-3-030-47679-3_18 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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eng |
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11 páginas |
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Cartagena de Indias |
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Arrieta Rodríguez, Eugenia82d66ae8-db71-43c7-bb5d-c7d7d9c3b619López-Martínez, Fernando2bd271e9-44ba-4016-8ebc-d28833dad946Martínez Santos, Juan Carlos480cc438-b02b-45d2-bfc9-7bfa96f0271bCartagena de Indias2020-09-10T21:23:04Z2020-09-10T21:23:04Z2020-05-222020-09-07Arrieta 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_18978-3-030-47679-3https://hdl.handle.net/20.500.12585/937910.1007/978-3-030-47679-3_18Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThere 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.11 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Science, vol 12133.A Machine Learning Approach for Severe Maternal Morbidity Prediction at Rafael Calvo Clinic in Cartagena-Colombiainfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionOtrohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Severe maternal morbidityMachine learningLogistic regressionSupport Vector MachineCartagena de IndiasPúblico generalCaicedo-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_21Calvert, J.S., et al.: A computational approach to early sepsis detection. Comput. Biol. Med. 74, 69–73 (2016)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)Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Mach. Learn. 31(1), 1–38 (2004)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)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)Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)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)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)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)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)Ng, A.: Machine learning: Stanford machine learning course materialsNilashi, 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)World Health Organization, UNICEF: Revised 1990 estimates of maternal mortality: a new approach. World Health Organization (1996)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)Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)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)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_22Tsui, A.O., Wasserheit, J.N., Haaga, J.G., et al.: Healthy pregnancy and childbearing (1997)Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)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)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. 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