1. Skou, J., Kesmodel, U., Henriksen, T.B., Secher, N.J.: An increasing proportion of infants weight more than 4000 grams at birth. Acta Obstet. Gynecol. Scand.80(10), 931–936 (2001)

   Article  Google Scholar 

2. Das, U.S.G., Sysyn, G.D.: Abnormal fetal growth: intrauterine growth retardation, small for gestational age, large for gestational age. Pediatr. Clin. North Am.51(3), 639–654 (2004)

   Article  Google Scholar 

3. Vasileios, G., Eleni, E., Anna, C., Dimitrios, K., Aikaterini, D., Styliani, A.: Serum adiponectin and leptin levels and insulin resistance in children born large for gestational age are affected by the degree of overweight. Clin. Endocrinol.66(3), 353–359 (2007)

   Article  Google Scholar 

4. Langer, O.: Fetal macrosomia: etiologic factors. Clin. Obstet. Gynecol.43(2), 283–297 (2000)

   Article  Google Scholar 

5. Battaglia, F.C., Lubchenco, L.O.: A practical classification of newborn infants by weight and gestational age. J. Pediatr.71(2), 159–163 (1967)

   Article  Google Scholar 

6. Boulet, S.L., Alexander, G.R., Salihu, H.M., Pass, M.: Macrosomic births in the United States: determinants, outcomes, and proposed grades of risk. Am. J. Obstet. Gynecol.188(5), 1372–1378 (2003)

   Article  Google Scholar 

7. Raio, L., Ghezzi, F., Naro, E.D., Buttarelli, M., Franchi, M., Drig, P., Brhwiler, H.: Perinatal outcome of fetuses with a birth weight greater than 4500 g: an analysis of 3356 cases. Eur. J. Obstet. Gynecol. Reprod. Biol.109(2), 160–165 (2003)

   Article  Google Scholar 

8. Shen, Y., Zhao, W., Lin, J., Liu, F.: Accuracy of sonographic fetal weight estimation prior to delivery in a Chinese han population. J. Clin. Ultrasound45(8), 465–471 (2017)

   Article  Google Scholar 

9. Blue, N.R., Jmp, Y., Holbrook, B.D., Nirgudkar, P.A., Mozurkewich, E.L.: Abdominal circumference alone versus estimated fetal weight after 24 weeks to predict small or large for gestational age at birth: a meta-analysis. Am. J. Perinatol.34(11), 1115–1124 (2017)

   Article  Google Scholar 

10. Moore, G.S., Kneitel, A.W., Walker, C.K., Gilbert, W.M., Xing, G.: Autism risk in small- and large-for-gestational-age infants. Am. J. Obstet. Gynecol.206(4), 314.e1–314.e9 (2012)

    Article  Google Scholar 

11. Akhtar, F., Li, j., Yu, G., Imran, A., Azeem, M.: Monitoring bio-chemical indicators using machine learning techniques for an effective large for gestational age prediction model with reduced computational overhead. In: The 7th International Conference on Frontier Computing (FC 2018) - Theory, Technologies and Applications (2018)

    Google Scholar 

12. Akhtar, F., Li, J., Azeem, M., Chen, S., Pan, H., Wang, Q., Yang, J.J.: Effective large for gestational age prediction using machine learning techniques with monitoring biochemical indicators. J. Supercomput. (2019).https://doi.org/10.1007/s11227-018-02738-w

13. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res.12(10), 2825–2830 (2013)

    MathSciNet MATH  Google Scholar 

14. Zhang, S., Wang, Q., Shen, H.: Design implementation and significance of Chinese free pre-pregnancy eugenics checks project. Natl. Med. J. China95(3), 162–165 (2015)

    Google Scholar 

15. Adankon, M.M., Cheriet, M., Biem, A.: Semisupervised least squares support vector machine. IEEE Trans. Neural Netw.20(12), 1858–1870 (2009)

    Article  Google Scholar 

16. Bammann, K.: Statistical models: theory and practice. Biometrics62(3), 943–943 (2006)

    Article MathSciNet  Google Scholar 

17. Guo, L., Yang, J.J., Peng, L., Li, J., Liang, Q.: A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Comput. Ind.69, 72–80 (2015). Special Issue: Information Technologies for Enhanced Healthcare

    Article  Google Scholar 

18. Li, J., Lu, L., Sun, J., Mo, H., Yang, J.J., Shi, C., Liu, H., Wang, Q., Hui, P.: Comparison of different machine learning approaches to predict small for gestational age infants. IEEE Trans. Big DataPP(99), 1–14 (2016)

    Google Scholar 

19. Li, J., Zhao, S., Yang, J., Huang, Z., Liu, B., Chen, S., Pan, H., Wang, Q.: WCP-RNN: a novel RNN-based approach for bio-NER in Chinese EMRs. J. Supercomput. (2018).https://doi.org/10.1007/s11227-017-2229-x

20. Akhtar, F., Li, j., Pei, Y., Azeem, M.: A semi-supervised technique for LGA prognosis. In: Proceedings of The International Workshop on Future Technology FUTECH 2019, pp. 36–37 (2018)

    Google Scholar 

Download references