A Systematic Review on the Applications of Machine Learning for Fetal Birth Weight Prediction

Abstract

Deepak T. Mane, Jyoti Mante, Anuradha Amar Bakare, Yatin Gandhi, Vinit Khetani and Rupali Atul Mahajan

In order to protect the maternal and infant safety, birth weight is an important indicator during fetal development. A doctor's experience in clinical practice, however, helps estimate birth weight by using empirical formulas based on the experience of the doctors. Recently, birth weights have been predicted using machine learning (ML) technologies. A machine learning model is built on the basis of a collection of attributes learns to predict predefined characteristics or results. Using a machine learning model, input and output are modeled together and then a set of models are trained on the data. It is possible to use machine learning for a variety of tasks such as predicting risks, diagnosing diseases, and classifying objects due to its scalability and flexibility, which are advantages over conventional methods. This research reviews the machine learning classification models used previously by various researchers to predict fetal weight. In this paper 85 studies were reviewed. Machine learning approach was considered as a better option to predict the fetal weight in all the studies included in this paper. The findings of this research show that the accuracy rate of using machine learning applications for fetal birth weight prediction is above 60% in all the studies reviewed.

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