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Prediction of Child Birth Weight Using Kernel Extreme Reservoir Machine and QPSO for Optimization

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Abstract

Birth weight is considered a major factor when monitoring any signs of abnormalities in the growth of the fetus and taking timely decisions related to labor management. Existing methods involve specialized equipment and training, which makes them less feasible for underdeveloped areas. Therefore, this study proposed a system for prediction of childbirth weight through kernel extreme reservoir machines and optimized the model parameters by the use of particle swarm optimization. Experimental results showed a significant improvement in the recommended method over existing models. The proposed approach is more economical than the traditional ultrasound making it extremely suited to underprivileged communities.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Federal, Pakistan

    Ghalib Ahmed Tahir, Tooba Samad, Sundus Abrar & Hammad Qureshi

  2. Dalian Maritime University, 1 Linghai Rd, Ganjingzi District, Dalian, Liaoning, China

    Liu Zongying

  3. Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Eoeun-dong, Yuseong-gu, Daejeon, South Korea

    Murtaza Ashraf

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  1. Ghalib Ahmed Tahir
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  2. Tooba Samad
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Correspondence to Ghalib Ahmed Tahir.

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Tahir, G.A., Samad, T., Zongying, L. et al. Prediction of Child Birth Weight Using Kernel Extreme Reservoir Machine and QPSO for Optimization. SN COMPUT. SCI. 2, 218 (2021). https://doi.org/10.1007/s42979-021-00601-z

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