Author : Ayesha Siddika 1
Date of Publication :30th September 2023
Abstract: Maternal patients, such as when a mother thinks her blood pressure may rise, her glucose may rise, or fall, and there is a lot of data that can harm the baby. Here we analyzed that data and trained it with different machine learning algorithms. These are Xgboost, Decision Tree, Random Forest and Support Vector Machine, and Naive Bayes Algorithms. Maternal health risk analysis has been done by many, but no one has produced results that are more accurate than 88%. But 3 of our models show a better performance compared to the existing ones. We have 94% accuracy in those three models; these are Xgboost, Decision Tree and Random Forest Algorithm. At the same time, SVM has 72% accuracy and Naive Bayes has 64% accuracy.
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