Risk Level Prediction for Maternal Health Using Machine Learning Algorithms

Abstract

Machine learning (ML) is a subset of artificial intelligence (AI) that is booming nowadays. It has the ability to learn from various data available and make predictions about future events for unseen data. It possesses the capability to autonomously learn from past experiences. While machine learning algorithms (MLAs) are quite complex, their utility in today's world is substantial. This paper centers around the utilization of these MLAs to make predictions in the healthcare sector using available historical data that includes various health factors for demonstrating the risk associated with maternal health (MH). Using various MLAs and considering the health factors that are associated during pregnancy and post-delivery we aim to facilitate the implementation of preventive measures based on these predictions. MH is an issue of great concern globally. Many women globally are at risk every year due to lack of prevention and irresponsiveness towards health of women after delivery, this can also lead to several diseases and deaths. Hence, it is essential to diagnose the associated complications at an early stage for timely implementation of preventative measures, this not only expands the application of ML in healthcare but also has the potential to save lives and ensure the good well-being of mothers and newborn individuals. It helps to ascertain the challenges according to the pre health condition of everyone.