Predicting mothers' exclusive breastfeeding for the first 6 months: Interface creation study using machine learning technique.

ABSTRACT

BACKGROUND: Machine learning techniques (MLT) build models to detect complex patterns and solve new problems using big data. AIM: The present study aims to create a prediction interface for mothers breastfeeding exclusively for the first 6 months using MLT. METHOD: All mothers who had babies aged 6-24 months between 15.09.2021 and 15.12.2021 and to whom the surveys could be delivered were included. 'Personal Information Form' created by the researchers was used as a data collection tool. Data from 514 mothers participating in the study were used for MLT. Data from 70% of mothers were used for educational purposes, and a prediction model was created. The data obtained from the remaining 30% of the mothers were used for testing. RESULTS: The best MLT algorithm for predicting exclusive breastfeeding for the first 6 months was determined to be the Random Forest Classifier. The top five variables affecting the possibility of mothers breastfeeding exclusively for the first 6 months were as follows "the mother not having any health problems during pregnancy," "there were no people who negatively affected the mother's morale about breastfeeding," "the amount of water the mother drinks in a day," "thinking that her milk supply is insufficient," "having no problems breastfeeding the baby". CONCLUSIONS: Using created prediction model may allow early identification of mothers with a risk of not breastfeeding their babies exclusively for the first 6 months. In this way, mothers in the risk group can be closely monitored in the early period.