Abstract
In the large for gestational age infant’s classification and prediction, noisy features are distilled to improve the classifier performance. It is accomplished with the creation of a suitable feature vector followed by GridSearch-based Recursive Feature Elimination with Cross-Validation (RFECV) scheme. It attempts to elect features that are influential and independent. We executed experiments on the data obtained from the National Pregnancy and Examination Program of China (2010–2013). The results are compared with the results already reported in the literature. The GridSearch-based RFECV scheme exhibited smaller features subset size with an increased classifier performance. The precision and area under the curve (AUC) scores are drastically improved from 0.7134 and 0.7074 to 0.96 to 0.86 respectively. Therefore, pediatricians are suggested to use fifty-three features subset, ranked by GridSearch-based RFECV scheme using Support Vector Machine (SVM) for the establishment of an efficient LGA prognosis process.
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References
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)
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)
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)
Langer, O.: Fetal macrosomia: etiologic factors. Clin. Obstet. Gynecol. 43(2), 283–297 (2000)
Battaglia, F.C., Lubchenco, L.O.: A practical classification of newborn infants by weight and gestational age. J. Pediatr. 71(2), 159–163 (1967)
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)
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)
Shen, Y., Zhao, W., Lin, J., Liu, F.: Accuracy of sonographic fetal weight estimation prior to delivery in a Chinese han population. J. Clin. Ultrasound 45(8), 465–471 (2017)
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)
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)
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)
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
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(10), 2825–2830 (2013)
Zhang, S., Wang, Q., Shen, H.: Design implementation and significance of Chinese free pre-pregnancy eugenics checks project. Natl. Med. J. China 95(3), 162–165 (2015)
Adankon, M.M., Cheriet, M., Biem, A.: Semisupervised least squares support vector machine. IEEE Trans. Neural Netw. 20(12), 1858–1870 (2009)
Bammann, K.: Statistical models: theory and practice. Biometrics 62(3), 943–943 (2006)
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
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 Data PP(99), 1–14 (2016)
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
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)
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This study is supported by the Beijing Nature Science Foundation of China(Z160003).
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Akhtar, F., Li, J., Pei, Y., Xu, Y., Rajput, A., Wang, Q. (2020). Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_8
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