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Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061116.

A task-prediction model based on the random forest algorithm


Shuo Ma, Zhihao Zhao, Junfei Sun

Corresponding Author:
Shuo Ma

Inner Mongolia University of Science and Technology, Baotou, China


The impact of maternal physical and mental health on infants is an important research field. We investigated the effects of maternal physical and psychological indicators on infant sleep quality through Spearman correlation analysis cluster analysis (K-Means) and random forest algorithm. The relative coefficient between each variable is calculated by organizing and optimizing the relevant data. At the same time, by optimizing the model to accurately predict and make optimal adjustments, the sleep quality of infants can be improved. By using a model to classify and predict training and testing data, and evaluating the accuracy, recall, accuracy, and F1 value of the model, it is concluded that there is a practical relationship between the mother's age, EPDS score, marital status, and other characteristics with the rating.


Physical indicators, psychological indicators, sleep quality, random forest algorithm

Cite This Paper

Shuo Ma, Zhihao Zhao, Junfei Sun. A task-prediction model based on the random forest algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 124-132. https://doi.org/10.25236/AJCIS.2023.061116.


[1] Scientific Platform Serving for Statistics Professional 2021. SPSSPRO. (Version1.0.11) [Online Application Software]. Retrieved from https://www.spsspro.com.

[2] Xu Weichao. Overview of Research on Correlation Coefficients [J]. Journal of Guangdong University of Technology, 2012, 29 (3): 12-17

[3] Zhou Zhihua. Machine Learning [M]. Tsinghua University Press, 2016

[4] Saroj, Kavita. Review: study on simple k mean and modified K mean clustering technique [J]. International Journal of Computer Science Engineering and Technology, 2016, 6(7):279- 281.