Abstract
We propose a cluster-based feature selection (CFS) scheme to establish an efficient prognosis process for the identification of a Macrosomia fetus. Macrosomia fetus adheres numerous complications during and after the antepartum period and is among established reasons for neonate mortality. Almost all of the classifiers with the proposed CFS scheme elevated macrosomia prediction scores compare to previously published studies. The prediction scores are increased by \(+4\%\) and \(+12\%\) in terms of precision and Area under the curve which authenticates the applied scheme. Therefore, we suggest pediatricians use CFS scheme with Support Vector Machine (SVM) for develo** better prognosis process to develop the best macrosomia prediction framework.
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This study is supported by the National Key R&D Program of China with project no. 2017YFB1400803.
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Akhtar, F., Li, J., Pei, Y., Siraj, S., Shaukat, Z. (2020). Macrosomia Fetus Prediction with Cluster-Based Feature Selection 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_7
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DOI: https://doi.org/10.1007/978-981-15-3250-4_7
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