APA (7th ed.) Citation

Taye, E. A., Woubet, E. Y., Hailie, G. Y., Arage, F. G., Zerihun, T. E., Zegeye, A. T., . . . Kassaw, A. T. (2025). Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis. BMC public health, 25(1), 901-14. https://doi.org/10.1186/s12889-025-22007-9

Chicago Style (17th ed.) Citation

Taye, Eliyas Addisu, Eden Yitbarek Woubet, Gabrela Yimer Hailie, Fetlework Gubena Arage, Tigabu Eskeziya Zerihun, Adem Tsegaw Zegeye, Tarekegn Cheklie Zeleke, and Abel Temeche Kassaw. "Application of the Random Forest Algorithm to Predict Skilled Birth Attendance and Identify Determinants Among Reproductive-age Women in 27 Sub-Saharan African Countries; Machine Learning Analysis." BMC Public Health 25, no. 1 (2025): 901-14. https://doi.org/10.1186/s12889-025-22007-9.

MLA (9th ed.) Citation

Taye, Eliyas Addisu, et al. "Application of the Random Forest Algorithm to Predict Skilled Birth Attendance and Identify Determinants Among Reproductive-age Women in 27 Sub-Saharan African Countries; Machine Learning Analysis." BMC Public Health, vol. 25, no. 1, 2025, pp. 901-14, https://doi.org/10.1186/s12889-025-22007-9.