Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables' contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. To provide an example of the different algorithms' function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Automated summary

Machine learning, as a branch of artificial intelligence, is increasingly utilized in health research to better understand risk groups, select model variables, and assess variable contributions to outcomes. This paper describes three specific machine learning algorithms in detail and discusses both their advantages and drawbacks in the context of maternal outcomes research. The algorithms were applied to a completed cross-sectional study to examine their performance compared to traditional methods in assessing the association of oxytocin total dose exposure with primary cesarean section.