Predicting premature birth during pregnancy using machine learning: A systematic review

Muhammad Modi Lakulu

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Type :	Article
Subject :	T Technology (General)
ISBN :	2147-6799
Main Author :	Muhammad Modi Lakulu
Additional Authors :	Ismail @ Ismail Yusuf Panessai
Title :	Predicting premature birth during pregnancy using machine learning: A systematic review
Hits :	226

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	International Journal of Intelligent Systems and Applications in Engineering
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
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Abstract : Universiti Pendidikan Sultan Idris

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 algorithms with results obtained with an accuracy of 0.95, recall of 1.0, and specificity of 0.94 using rf. To prevent preterm birth, it is critical to support research in this area and develop machine learning-based solutions with broad clinical applicability. It is also advised that future research compare ml with a traditional approach using the same data to comprehend its value in filling the current gap. This comprehensive review makes a substantial contribution to the specialized literature on women's health and artificial intelligence. © 2024, Ismail Saritas. All rights reserved.

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