Published Paper Details:

In the context of Bangladesh, this work has created a system for accurately monitoring and forecasting a pregnant woman's risk level. Pregnant women's health information and risk factors will be examined by this method to determine the risk intensity level. By 2030, the United Nations wants to lower mother and infant deaths and improve maternal health, but the rate is not declining as quickly as it should. This study evaluated the risk level based on risk factors in pregnancy using the relevant analytical tools and machine learning algorithms. Data on maternal health was acquired for this study from the UCI machine learning library. Risk has been examined using categorization and classification techniques based on risk level. The Random Forest Algorithm provides the highest accuracy of 96% for training data, when it comes to classification and prediction of the risk level, according to a comparison of certain groups of machine learning algorithms.