Different Machine Learning Prediction Models using Pregnancy Risk Prediction Systems

Maternal Mortality is declared as a major problem for women living in rural areas. The requirement to travel a long distance and the scarcity of primary healthcare providers like medicines, highly qualified healthcare doctors, and experienced nurses have posed a main limitation to the healthcare sector. Generally, early detection and administration of the risks connected with pregnancy are important to provide the best treatment to pregnant women. High pregnancy risks lead to premature birth results and sudden death in a relative percentage of affected women. While verifying patients at risk phase could give prevention strategies and help in efficient clinical analysis for remedy. This article analysed complications, challenges based on pregnancy risk prediction, and different methods. Several prediction methods include DT, SVM, KNN, and NB. Analysis of the decision tree classifier model has achieved a high accuracy rate of 93% compared to the other models.