A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings

Maternal health is a serious concern for many nations due to a lack of appropriate healthcare facilities, healthcare staff, and late diagnoses of life-threatening diseases. Pregnant women suffer with numerous challenges during the pregnancy and childbirth. Non-communicable diseases, a lack of nutrition in diets, and unawareness of the risks associated with pregnancy are the primary reasons for these challenges. Sometimes these reasons become a direct cause of maternal mortality as well. Awareness of the risks and early detection may contribute to a reduction in maternal deaths during pregnancy and childbirth. Various ICTs have been incorporated into the healthcare industry to diagnose the issue as quickly as is feasible and an appropriate remedy can be initiated to treat diseases. Machine Learning (ML) techniques have the potential to predict the probable risk factors for timely interventions; however, challenge arises when the data are limited and unstructured. The Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) algorithms, with 10-fold cross validation, are used in this study. The dataset utilized in this study included both the present and past medical histories and important vitals of pregnant women. With a test score of 98.8%, the Decision Tree (DT) algorithm outperformed other algorithms, according to the results. Based on the predicted result, pregnant women can consult with medical specialists for their consultation to reduce the potential difficulties in the near future.