Preterm Baby Birth Prediction using Machine Learning Techniques

Abstract

Premature or preterm is the word that refers to early. Consequently, preterm birth is the birth of a baby inborn earlier than 37 weeks of maternity. Premature birth is one of the foremost important influential factors in infant death. It is approximate 15 million premature babies square measure born every year; growing this number of pre-term babies has a more significant impact on developing countries. As a result, predicting preterm birth may be an active research field. Although pre-term babies have several health issues, early identification of the factors for pre-term delivery will decrease the number of premature babies and mothers will also know the reasons which are causing premature babies. During this research, we've got developed a system to predict pre-term babies. Initially, we tend to study and recognized the main factors corresponding to premature babies with the consultancies of specialized doctors. The main factors are the mother's weight before pregnancy, mother's age, number of the previous preemie, mother's BMI, cervical problem, etc. After that, the dataset is pre-processed and normalized. Finally, four binary classifiers i.e. KNN, Decision Tree, SVM, and Naïve Bayes are trained and tested. The investigation result shows the effectiveness of the projected system with 99% accuracy.