Early Prediction of Maternal Health Risk Factors Using Machine Learning Techniques

Abstract

Nowadays, maternal health issues are one of the most challenging issues all over the world. Many women die each year during pregnancy and after delivery, which is a major cause of infant mortality. In rural areas,pregnant women face various difficulties and challenges, including a shortage of doctors, inadequate knowledge, a lack of public clinics, infrastructure issues, and transportation issues. The mother's pregnancy is the major cause of the infant's poor health, rather than any other factors that may have arisen after childbirth. Significant roles are played by maternal risk factors such as the mother's chronic condition, age, nutrition, and other medical assistance during pregnancy. Recent developments in Artificial intelligence methods, particularly machine learning models, have made it easier to make predictions in a variety of disciplines. We can identify the primary maternal risk factors that can lead to newborn child and maternal mortality using machine learning techniques. This paper proposes improved data preprocessing methods that involve feature engineering and data cleaning in order to effectively handle anomalies in the data values. To identify the maternal health risk factor, several machine learning algorithms were used, including Cat Boost, Random Forest, XGB, Decision Tree, and Gradient Boost. Using the preprocessed dataset, the suggested model was developed, trained, and tested. The Random Forest was the best machine-learning algorithm with an accuracy score of 90%, precision (90%), recall (90%), and Fl-score (90%)