Machine Learning-based maternal health risk prediction model for IoMT framework

Subhash Mondal; Amitava Nag; Anup Kumar Barman; Mithun Karmakar

doi:10.52756/ijerr.2023.v32.012

Authors

Subhash Mondal Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0002-4203-8467
Amitava Nag Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0003-4408-7307
Anup Kumar Barman Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0002-5697-0431
Mithun Karmakar Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, Kokrajhar, Assam, India https://orcid.org/0000-0003-0683-8800

DOI:

https://doi.org/10.52756/ijerr.2023.v32.012

Keywords:

Maternal Health Risk, Internet of Medical Things (IoMT), Prediction Model, Exploratory Data Analysis (EDA), Android-based Application, Random Forest Classifier

Abstract

The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices provide a means to collect real-time health data, enabling continuous monitoring and analysis in the Internet of Medical Things (IoMT) environments. By integrating IoT devices into the system, crucial signs such as Heart Rate (HR), Systolic and Diastolic Blood Pressure (BP), Fetal Movements (FM), and Temperature (T) can be tracked remotely and non-invasively. This allows for the timely detection of abnormalities or potential risk factors during pregnancy, empowering healthcare professionals to intervene proactively and provide personalized care. This research focuses on developing a system for observing and predicting the maternal risk level in the IoT environment, mainly in remote areas. The goal is to improve maternal health and reduce maternal and child mortality rates, a significant decline according to United Nations targets for 2030. The research utilizes analytical tools and Machine Learning (ML) algorithms to analyze health data and risk factors associated with pregnancy. The acquired dataset contains various risk factors categorized and classified based on intensity. After comparing different ML models’ experimental results, Exploratory Data Analysis (EDA) approaches to determine the most effective risk factors. The fine-tuned Random Forest Classifier (RF) achieves the highest accuracy of 93.14%. An Android-based application has also been developed to deploy the prediction model to determine risk levels based on the different parameters.

References

Ahmed, M. (2022). Maternal Health Risk Data. From Kaggle: https://www.kaggle.com/datasets/csafrit2/maternal-health-risk-data

Ahmed, M., Abul Kashem, M., Rahman , M., & Khatun , S. (2020). Review and Analysis of Risk Factor of Maternal Health in Remote Area Using the Internet of Things (IoT). InECCE2019, pp. 357–365. https://doi.org/10.1007/978-981-15-2317-5_30

Assaduzzaman, M., Al Mamun, A., & Hasan, M. (2023). Early Prediction of Maternal Health Risk Factors Using Machine Learning Techniques. 2023 International Conference for Advancement in Technology (ICONAT). Goa, India. https://doi.org/10.1109/ICONAT57137.2023.10080700

Celdrán, A., Gil Pérez, M., García Clemente, F., & Martínez Pérez, G. (2018). Sustainable securing of Medical Cyber-Physical Systems for the healthcare of the future. Sustainable Computing: Informatics and Systems, 19, 138-146. https://doi.org/10.1016/j.suscom.2018.02.010

Pereira, S., Costa Filho, R., Ramos, R., & Oliveira, M. (2020). Improving Maternal Risk Analysis in Public Health Systems. 5th International Conference on Smart and Sustainable Technologies (SpliTech). Split, Croatia. https://doi.org/10.23919/SpliTech49282.2020.9243769

Raza, A., Rehman Siddiqui, H., Munir, K., & Almutairi, M. (2022). Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction. PLoS ONE, 17(11), e0276525.

https://doi.org/10.1371/journal.pone.0276525

Akbulut, A., Ertugrul, E., & Topcu, V. (2018). Fetal health status prediction based on maternal clinical history using machine learning techniques. Computer Methods and Programs in Biomedicine, 163, 87-100. https://doi.org/10.1016/j.cmpb.2018.06.010

Al-Jaroodi, J., Mohamed, N., & Abukhousa, E. (2020). Health 4.0: On the Way to Realizing the Healthcare of the Future. IEEE Access, 8, 211189 - 211210. https://doi.org/10.1109/ACCESS.2020.3038858

Castillejo, P., Martinez, J. F., Rodriguez-Molina, J., & Cuerva, A. (2013). Integration of wearable devices in a wireless sensor network for an E-health application. IEEE Wireless Communications, 20(4), 38 - 49. https://doi.org/10.1109/MWC.2013.6590049

Chen, H.-Y., Chuang, C. H., Yang, Y. J., & Wu, T. P. (2011). Exploring the risk factors of preterm birth using data mining. Expert Systems with Applications, 38(5), 5384-5387. https://doi.org/10.1016/j.eswa.2010.10.017

Gupta, R., Shukla, A., Mehta, P., & Bhattacharya, P. (2020). VAHAK: A Blockchain-based Outdoor Delivery Scheme using UAV for Healthcare 4.0 Services. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Toronto, ON, Canada. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162738

Haleem, A., Javaid, M., Singh, R., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12-30.

https://doi.org/10.1016/j.iotcps.2022.04.001

Hussain, T. M., Shaikh, M., Ali, B. R., & Talpur, H. (2014). Internet of Things as Intimating for Pregnant Women’s Healthcare: An Impending Privacy Issues. The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 12(6), 4337-4344. https://doi.org/10.11591/telkomnika.v12i6.4227

Islam, R., Sayed, M., Saha, S., & Jamal Hossain, M. (2023). Android malware classification using optimum feature selection and ensemble machine learning. Internet of Things and Cyber-Physical Systems, 3, 100-111. https://doi.org/10.1016/j.iotcps.2023.03.001

Jaleel, A., Mahmood, T., Awais Hassan, M., & Bano, G. (2020). Towards Medical Data Interoperability Through Collaboration of Healthcare Devices. IEEE Access, 8, 132302 - 132319. https://doi.org/10.1109/ACCESS.2020.3009783

Oliveira, M.-Y., Pesqueira, A., Sousa, M., & Dal Mas, F. (2021). The Potential of Big Data Research in HealthCare for Medical Doctors’ Learning. Journal of Medical Systems, 45(13). https://doi.org/10.1007/s10916-020-01691-7

Pang, Z., Yang, G., Khedri, R., & Zhang, Y.T. (2018). Introduction to the Special Section: Convergence of Automation Technology, Biomedical Engineering, and Health Informatics Toward the Healthcare 4.0. IEEE Reviews in Biomedical Engineering, 11, 249 - 259. https://doi.org/10.1109/RBME.2018.2848518

Pawar, L., Malhotra, J., Sharma, A., & Arora, D. (2022). A Robust Machine Learning Predictive Model for Maternal Health Risk. 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC). Coimbatore, India. https://doi.org/10.1109/ICESC54411.2022.9885515

Pereira, S., Portela, F., & Santos, M. (2015). Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining☆. Procedia Computer Science, 64, 601-609. doi:10.1016/j.procs.2015.08.573

Rani , S., & Kumar, M. (2021). Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT. Multimedia Tools and Applications, 80, 24555–24571. https://doi.org/10.1007/s11042-021-10823-1

Rawashdeh, H., Awawdeh, S., Shannag, F., & Henawi, E. (2020). Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Computational Biology and Chemistry, 85, 107233. https://doi.org/10.1016/j.compbiolchem.2020.107233

Redondi, A., Chirico, M., Borsani, L., & Cesana, M. (2013). An integrated system based on wireless sensor networks for patient monitoring, localization and tracking. Ad Hoc Networks, 11(1), 39-53.

https://doi.org/10.1016/j.adhoc.2012.04.006

Sarhaddi, F., Azimi , I., Labbaf , S., & Niela-Vilén, H. (2021). Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation. Sensors, 21(17), 2281. https://doi.org/10.3390/s21072281

WHO.(2023). Maternal mortality. Retrieved June 01, 2023 from https://www.who.int/news-room/fact-sheets/detail/maternal-mortality