Prediction and Influencing Factors of Residents' Ideal Childbirth: Feature Selection Based on Random Forest

DOI

10.2991/978-94-6463-042-8_101How to use a DOI?

Keywords

Ideal Fertility; Data Mining; Random Rorest; Feature Selection

Abstract

As the number of new births in China continued to decline from 2017 to 2021, the focus on fertility is particularly important. This paper summarizes four categories of predictor variables from the literature, namely basic personal characteristics, residents' original family characteristics, occupational characteristics and couple relationship characteristics, a total of 16 variables, and screened out the more important 12 variables based on random forest feature selection. The study found that: (1) The variables among the occupational characteristics, original family characteristics and personal basic characteristics of residents have a great influence on the ideal number of children. (2) In the prediction analysis, the support vector machine with linear kernel function has the best prediction effect, and has obvious advantages over logistic regression and random forest. The results of the study are of significance for understanding China's fertility level and alleviating the decline of the newborn population.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Liu Yu
PY  - 2022
DA  - 2022/12/29
TI  - Prediction and Influencing Factors of Residents' Ideal Childbirth: Feature Selection Based on Random Forest
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 704
EP  - 709
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_101
DO  - 10.2991/978-94-6463-042-8_101
ID  - Yu2022
ER  -