Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT

Abstract

To save the risk during the pregnancies in remote areas (where women cannot approach the doctors in the urban areas for proper check-ups), the authors have proposed an IoT based remote monitoring of pregnant ladies where the data is collected at the cloud server. Machine learning techniques will be applied on the trimesters’ attributes to find out the reasons of mortality rate of the babies. The use of these advanced technologies in pregnant women care environment can absolutely eradicate the pregnancy complications and harmful incidents. An initial work towards this study is to assess mortality risk prediction in pregnant ladies using machine-learning algorithms for proper prediction and treatment on time. A dataset of 10,000 pregnant women is analysed in this study. Classification algorithms are used to check the death rate of new born babies based on the mother’s age. The survival ratio is presented by applying the various algorithms. Two class SVM model is presented as the most accurate prediction model which outperformed over boosted decision tree, average perceptron, decision forest, locally deep SVM, Bayes point machine, decision forest and logistic regression. Age of the mother and frequency of the pregnancy without proper gap (i.e. less than 3 years) make impact on the mortality rate of the babies. Among the various algorithms, the AUC value of decision forest is augmented at 91.4% whereas two class SVM shows significantly improved performance to 96.6%. The authors have proposed machine learning-based final model, death rate of infants, frequency of pregnancy and age of the mother were interrelated as notable risk factors for mortality in Indian ladies along with other issues.