Abstract
The progress of science and technology in recent times has gifted numerous solutions that have revolutionized our lifestyle. The advent of Internet of Things (IoT) and associated frameworks have ushered in a new era of smart, automated applications that require minimum human intervention to provide effective solutions. However, it is quintessential that some critical, real-time, human health related problems be addressed in a human-centric approach with the aid of the current technological developments. The assessment and early detection of maternal risk is a persisting issue which often results in loss of life and/or trauma in pregnant women. The proposed work addresses this problem with the development of a smart system that is capable of identifying the intensity of maternal risk based on physiological health parameters of the patients. It is observed that the Decision Tree classification algorithm is more effective in developing such a critically important system, than other algorithms like Logistic Regression and Multi-Layer Perceptron. Our system thus provides an efficient solution that can mitigate mis-identification of maternal risk intensity to a large extent whilst ensuring an accuracy of 83.5%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
UNICEF.: Maternal mortality (2019). https://data.unicef.org/topic/maternal-health/maternal-mortality/, Accessed 10 Jan 2022
Allahem, H., Sampalli, S.: Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning. Inform. Med. Unlocked 28(100), 771 (2022). https://doi.org/10.1016/j.imu.2021.100771
Liu, J., Wang, C., Yan, R., Lu, Y., Bai, J., Wang, H., Li, R.: Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve. Arch. Gynecol. Obstet. 1–11 (2022)
Rousseau, S., Polachek, I.S., Frenkel, T.I.: A machine learning approach to identifying pregnant women’s risk for persistent post-traumatic stress following childbirth. J. Affect. Disord. 296, 136–149 (2022). https://doi.org/10.1016/j.jad.2021.09.014
Zheutlin, A.B., Vieira, L., Shewcraft, R.A., Li, S., Wang, Z., Schadt, E., Gross, S., Dolan, S.M., Stone, J., Schadt, E., Li, L.: Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records. J. Am. Med. Inform. Assoc. 29(2), 296–305 (2021). https://doi.org/10.1093/jamia/ocab161, https://academic.oup.com/jamia/article-pdf/29/2/296/42180056/ocab161.pdf
Hoffman, M., Liu, W., Tunguhan, J., Bitar, G., Kumar, K., Ewen, E.: Machine learning algorithm using clinical data and demographic data for preterm birth prediction. Am. J. Obstet. Gynecol. 226(1), S362–S363 (2022)
Biswas, S., Shukla, S.: A miscarriage prevention system using machine learning techniques. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds.) Proceedings of 2nd Doctoral Symposium on Computational Intelligence, pp. 423–433. Springer, Singapore (2022)
Attwaters, M.: Detecting pregnancy complications from blood. Nat. Rev. Genet. 1 (2022)
Clapp, M.A., James, K.E., McCoy, T.H., Perlis, R.H., Kaimal, A.J.: The value of intrapartum factors in predicting maternal morbidity. American Journal of Obstetrics and Gynecology MFM 4(1):100,485 (2022). https://doi.org/10.1016/j.ajogmf.2021.100485
Ahmed, M., Kashem, M.A., Rahman, M., Khatun, S.: Review and analysis of risk factor of maternal health in remote area using the internet of things (IoT). In: Kasruddin Nasir, A.N., Ahmad, M.A., Najib, M.S., Abdul Wahab, Y., Othman, N.A., Abd Ghani, N.M., Irawan, A., Khatun, S., Raja Ismail, R.M.T., Saari, M.M., Daud, M.R., Mohd Faudzi, A.A. (eds.) In: ECCE2019, pp. 357–365. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2317-5_30
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chakraborty, A., Dutta, S., Biswas, A., Das, P., Bhagat, S.N., Guha, S. (2023). MATRA: An Automated System for MATernal Risk Assessment. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_15
Download citation
DOI: https://doi.org/10.1007/978-981-19-5403-0_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5402-3
Online ISBN: 978-981-19-5403-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)