Artificial intelligence is widely developed in the health sector, and machine learning has been increasingly used in healthcare to make predictions, assign diagnoses and as a method of prioritizing actions. machine learning methods have become a feature of several tools in the field of obstetrics and child care. Is to identify the applicability and performance of machine learning methods used to identify preterm labor during pregnancy the main precision metric used is the AUC. the machine learning method with the best results was the prediction of prematurity the SVM classifier algorithm method is the best method for predicting the incidence of premature birth with an accuracy level of 0.997, recall of 0.995, and specificity of 1.0, for identifying a diagnosis of premature birth which is quite good. good. accurately. These results are similar to the results of Rawashdeh et al.'s research on a data mining-based intelligence system using the Naïve Bayes, Decision Tree, K-NN, RF, And NN .....

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Premature birth remains a significant challenge in maternal health services, underscoring the need for effective predictive models to enable early detection and intervention. This study examined the performance of four machine learning algorithms: Decision Trees, Naive Bayes, KNN, and Random Forest in predicting preterm birth during pregnancy. Utilizing data collected from pregnant individuals, encompassing maternal health indicators and fetal development metrics, our model aimed to forecast the likelihood of preterm birth. We assessed the predictive ability of each model by evaluating metrics such as accuracy, precision, recall, sensitivity, specificity, and area under the curve (AUC). The results revealed variability in model performance, with Logistic Random Forest exhibiting strong performance. This suggests its potential utility in clinical settings for the early detection and intervention of preterm pregnancy. Our study contributes to advancements in predictive modelling within m.....

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