Maternal Health Risk Prediction Based on Health Checkup Using Machine Learning Approaches

Abstract

Maternal health difficulties are currently one of the most difficult challenges in the world. Every year, many women die during pregnancy and after childbirth, which is a primary source of infant mortality. Maternal risk factors such as the mother's chronic illness, blood pressure, mental health, diet, and other medical care during pregnancy all play important roles. Pregnant women in remote locations confront several obstacles and challenges, including a scarcity of doctors, insufficient expertise, a lack of accessible clinics, infrastructural constraints, and transportation issues. The infant's poor health is mostly due to the mother's pregnancy, rather than any additional issues that may have occurred following childbirth. Using machine learning approaches, the study has predicted the maternal health risk level in previous due to avoid uncertain birth death or any inconvenience of a new born child. A variety of pre-trained advanced machine learning techniques were utilized in the study to find out the sustainable result. ANN, Ridge Classifier, SGD, XGBoost, Cat Boost, Random Forest, XGB, Decision Tree, and more algorithms were implemented. The recommended model was created, trained, and tested on the preprocessed dataset with the help of Hyper Parameter Tuning. The Cat Boost Classifier was the most accurate machine learning system for the study with a score of 97.4%.