• Dr.Harish B G
• Chetan Kumar G S
• Sushma N
• Sabeeha Banu N R
• Priyanka N B ,Shreyanka M N

Developing a predictive system to assess the risk level of pregnant women and classify fetal health can greatly contribute to the overall well-being of both the mother and the baby. The objective of this research is to utilize different machine learning classification models such as Naïve Bayes, K-Nearest Neighbor, Decision tree, and Random Forest to forecast potential complications during pregnancy, with the ultimate goal of reducing maternal mortality rates. The maternal health dataset was open access from the Kaggle website serves as a fundamental building block and various attributes were collected from expectant women. Datasets are analyzed then trained, and build the model. This model is hosted on a Flask web server, and users can predict the result using an easy-to-use GUI. A comparison of many machine learning classification algorithms reveals that the Decision Tree Algorithm has more accuracy in terms of fetus health classification and maternal risk prediction, with a numerical value of 93%.

Fetal, Fetal Health, Maternal Risk, Kaggle, Machine Learning, Flask Web Server.

"Pregnancy Risk and Fetal Health Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.h114-h121, July-2023, Available :http://www.jetir.org/papers/JETIR2307713.pdf

"Pregnancy Risk and Fetal Health Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. pph114-h121, July-2023, Available at : http://www.jetir.org/papers/JETIR2307713.pdf