Predicting preterm birth using machine learning methods

Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning models for predicting the risk of preterm birth. Data were collected from 50 patients in a maternity ward, with an analysis performed based on the timing of delivery (preterm vs. term). The applicability of XGBoost, CatBoost, logistic regression, support vector machines (SVM), and decision trees for predicting preterm delivery was evaluated through training. The linear SVM with boosted parameters demonstrated the highest performance, achieving an accuracy of 82%, precision of 83%, recall of 86%, and an F1-score of 84%. The logistic regression model, also boosted, demonstrated comparable performance to the linear SVM, with similar accuracy (80%), precision (82%), recall (82%), and F1-score (82%). The performance of other models, including decision trees and more complex algorithms, was inferior, which is likely attributable to the limited dataset and the number of parameters involved. In particular, machine learning models, most notably the linear SVM, can be effectively employed to assess the risk of preterm birth. The findings indicate that the linear SVM model exhibits the greatest efficacy among the tested models.