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Predicting Low Birth Weight Babies Through Data Mining

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New Knowledge in Information Systems and Technologies (WorldCIST'19 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 932))

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Low Birth Weight (LBW) babies have a high risk of developing certain health conditions throughout their lives that affect negatively their quality of life. Therefore, a Decision Support System (DSS) that predicts whether a baby will be born with LBW would be of great interest. In this study, six different Data Mining (DM) algorithms are tested for five different scenarios. The scenarios combine information about the mother’s physical characteristics and habits, and the gestation. Results are promising and the best model achieved a sensitivity of 91,4% and a specificity of 99%. Good results were also achieved without considering the gestational age, which showed that the use of DM might be a good alternative to the traditional medical imaging exams in the prediction of LBW early in the pregnancy.

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  1. Poole, K.L., Schmidt, L.A., Missiuna, C., Saigal, S., Boyle, M.H., Van Lieshout, R.J.: Childhood motor coordination and adult psychopathology in extremely low birth weight survivors. J. Affect. Disord. 190, 294–299 (2016).

    Article  Google Scholar 

  2. Mañalich, R., Reyes, L., Herrera, M., Melendi, C., Fundora, I.: Relationship between weight at birth and the number and size of renal glomeruli in humans: a histomorphometric study. Kidney Int. 58(2), 770–773 (2000).

    Article  Google Scholar 

  3. Wolke, D.: Born extremely low birth weight and health related quality of life into adulthood. J. Pediatr. 179, 11–12 (2016).

    Article  Google Scholar 

  4. de Castro, E.C.M., Leite, Á.J.M., de Almeida, M.F.B., Guinsburg, R.: Perinatal factors associated with early neonatal deaths in very low birth weight preterm infants in northeast brazil. BMC Pediatr. 14(1), 312 (2014)

    Article  Google Scholar 

  5. Bahado-Singh, R.O., Dashe, J., Deren, O., Daftary, G., Copel, J.A., Ehrenkranz, R.A.: Prenatal prediction of neonatal outcome in the extremely low-birth-weight infant. Am. J. Obstet. Gynecol. 178(3), 462–468 (1998).

    Article  Google Scholar 

  6. Perez-Roche, T., Altemir, I., Giménez, G., Prieto, E., González, I., Peña-Segura, J.L., Castillo, O., Pueyo, V.: Effect of prematurity and low birth weight in visual abilities and school performance. Res. Dev. Disabil. 59, 451–457 (2016).

    Article  Google Scholar 

  7. Dimassi, K., Douik, F., Ajroudi, M., Triki, A., Gara, M.F.: Ultrasound fetal weight estimation: how accurate are we now under emergency conditions? Ultrasound Med. Biol. 41(10), 2562–2566 (2015).

    Article  Google Scholar 

  8. Khalil, A., D’antonio, F., Dias, T., Cooper, D., Thilaganathan, B.: Ultrasound estimation of birth weight in twin pregnancy: comparison of biometry algorithms in the stork multiple pregnancy cohort. Ultrasound Obstet. Gynecol. 44(2), 210–220 (2014).

    Article  Google Scholar 

  9. Yadav, H., Lee, N.: Maternal factors in predicting low birth weight babies. Med. J. Malays. 68(1), 44–47 (2012)

    Google Scholar 

  10. Portela, F., Santos, M.F., Silva, Á., Rua, F., Abelha, A., Machado, J.: Preventing patient cardiac arrhythmias by using data mining techniques. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 165–170. IEEE (2014).

  11. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)

    Google Scholar 

  12. Brandao, A., Pereira, E., Portela, F., Santos, M.F., Abelha, A., Machado, J.: Predicting the risk associated to pregnancy using data mining. In: Proceedings of the International Conference on Agents and Artificial Intelligence, ICAART 2015, vol. 2, Lisbon, Portugal. SciTePress (2015)

    Google Scholar 

  13. Khademolqorani, S., Hamadani, A.Z.: An adjusted decision support system through data mining and multiple criteria decision making. Procedia Soc. Behav. Sci. 73, 388–395 (2013).

    Article  Google Scholar 

  14. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011).

    Book  MATH  Google Scholar 

  15. Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., Goy, A., Suh, K.S.: Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. J. Clin. Bioinform. 5(1), 4 (2015).

    Article  Google Scholar 

  16. Pereira, S., Portela, F., Santos, M.F., Machado, J., Abelha, A.: Predicting type of delivery by identification of obstetric risk factors through data mining. Procedia Comput. Sci. 64, 601–609 (2015)

    Article  Google Scholar 

  17. Naik, A., Samant, L.: Correlation review of classification algorithm using data mining tool: WEKA, Rapidminer, Tanagra, Orange and Knime. Procedia Comput. Sci. 85, 662–668 (2016)

    Article  Google Scholar 

  18. Yadav, S.K., Bharadwaj, B., Pal, S.: Data mining applications: a comparative study for predicting student’s performance. arXiv preprint arXiv:1202.4815 (2012)

  19. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: Crisp-dm 1.0 step-by-step data mining guide (2000)

    Google Scholar 

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This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.

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Correspondence to Hugo Peixoto .

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Loreto, P., Peixoto, H., Abelha, A., Machado, J. (2019). Predicting Low Birth Weight Babies Through Data Mining. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 932. Springer, Cham.

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