International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 949KB)
An Intelligent, Bilingual Pregnancy Health Monitoring System
PDF (949KB), PP.46-61
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Author(s)
Isah Omeiza Rabiu
1,*
Bitrus Judah Tanko
1
Nuhu Bello Kontagora
1
Index Terms
Bilingual System, Random Forest, Support Vector Machine (SVM), Expectant Mothers, Pregnancy Health
Abstract
This research implements an intelligent, bilingual pregnancy health monitoring system for expectant mothers. A significant problem commonly experienced by expectant mothers in rural areas in Nigeria is the unavailability of a decent antenatal system and a shortage of experienced medical personnel and equipment. The proposed system comprises IoT sensors, including Electrocardiogram (ECG), body temperature, and heart rate sensors, connected to an ESP32 microcontroller for data acquisition and transmission. A predictive system built using Random Forest and Support Vector Machine (SVM) classifiers categorises pregnancy risk into low, medium, and high. A Flask-based web application for real-time data visualization and diagnosis was developed to display the collected data and visually represent the risk level diagnosis. The performances of the predictive models, Random Forest and Support Vector Machine (SVM), were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved an accuracy surpassing SVMs by a margin of 5.28%. Random Forest and SVM precision were then compared and there was an improvement of 6.49%.
In addition, Random Forest had a higher recall than SVM by 6.58%, and also had a performance increase of 6.49% on F1-score as compared to SVM. The comparative analysis shows that the Random Forest model works better than SVM in all the main measures. In this project, the Random Forest model was better than the SVM because it uses ensemble learning to manage the non-linear relationship, imbalance data and noise better to achieve superior accuracy, recall, and the F1 Scores. It was also more reliable in categorizing risks in pregnancy, as it was interpretable, which was also strong and guaranteed the timely and suitable intervention of health care.
Cite This Paper
Isah Omeiza Rabiu, Bitrus Judah Tanko, Nuhu Bello Kontagora, "An Intelligent, Bilingual Pregnancy Health Monitoring System", International Journal of Education and Management Engineering (IJEME), Vol.16, No.3, pp. 46-61, 2026. DOI:10.5815/ijeme.2026.03.04
Reference
[1]World Health Organization, WHO recommendations on antenatal care for a positive pregnancy experience, Geneva, Switzerland: World Health Organization, 2016.
[2]R. D. Aliyu, E. I. Gloria, O. Adebowale, and I. Rebecca, "mHealth for self-management in pregnancy: Perceptions of women in low-resource settings," Procedia Comput. Sci., vol. 181, pp. 738–745, 2021, doi: 10.1016/j.procs.2021.01.226.
[3]P. Vinothiyalakshmi, V. Pallavi, N. Rajganesh, and V. Adityavignesh, V. “Internet of Things (IoT)-Based Smart Maternity Healthcare Services”, In Intelligent Systems and Sustainable Computational Models (pp. 266-274), 2024. Auerbach Publications.
[4]S. Ashraf, S. P. Khattak, and M. T. Iqbal, “Design and implementation of an open-source and Internet-of-Things-based health monitoring system”, Journal of Low Power Electronics and Applications, vol. 13, no. 4, 57, 2023.
[5]A. Bansal, V. A. Athavale, K. Kaur, and A. Garg, "IoT enabled prenatal health monitoring system for pregnant women," in Proc. 3rd Int. Conf. ICT Digit. Smart Sustain. Dev. (ICIDSSD), May 2023, p. 289.
[6]B. Dhanwanth, R. Saravanakumar, T. Tamilselvi, and K. Revathi, "A smart remote monitoring system for prenatal care in rural areas," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 3, pp. 30–36, 2023.
[7]S. G. Tenaw, D. Tsega, B. T. Zewudie et al., "Completion of the maternal continuum of care and its association with antenatal care attendance during previous pregnancy among women in rural areas of the Gurage Zone, Southwest Ethiopia: A community-based cross-sectional study," BMJ Open, vol. 12, p. e066536, 2022. doi: 10.1136/bmjopen-2022-066536.
[8]W. C. Yang, S. Sabwa, A. D. Mebratie, B. Amboko, I. Mugenya, S. Kim, ... and C. Arsenault, “Quality of antenatal care and perinatal outcomes: evidence from a cohort study in Ethiopia, Kenya, South Africa, and India”, medRxiv, 2025-04, 2025.
[9]H. Rehan, "Bridging the digital divide: A socio-technical framework for AI-enabled rural healthcare access in developing economies," Eur. Vantage J. Artif. Intell., vol. 2, no. 1, pp. 19–27, 2025.
[10]M. T. Sangy, M. Duaso, C. Feeley, and S. Walker, S. “Barriers and facilitators to the implementation of midwife-led care for childbearing women in low-and middle-income countries: A mixed-methods systematic review”, Midwifery, vol. 122, 103696, 2023.
[11]A. A. Abuosi, E. A. Anaba, A. A. Daniels, A. A. A. Baku, and J. Akazili, “Determinants of early antenatal care visits among women of reproductive age in Ghana: evidence from the recent Maternal Health Survey”, BMC pregnancy and childbirth, vol. 24, no. 1, 309, 2024.
[12]F. S. Tanberika, T. B. Sansuwito, and H. C. Hassan, “Knowledge in enhancing antenatal care compliance and improving maternal and neonatal health outcomes”, Journal of Angiotherapy, vol. 8, no. 12, 1-7, 2024.
[13]M. Nazari, H. Sadr, H. Emami, and R. Rabiei, "A telemonitoring system for high-risk pregnancies: A novel approach to enhancing prenatal care and reducing maternal and fetal complications," in Proc. 2025 11th Int. Conf. Web Res. (ICWR), Apr. 2025, pp. 431–437.
[14]R. Ettiyan, and V. Geetha, V. “Iod-Nets–An IoT based intelligent health care monitoring system for ambulatory pregnant mothers and fetuses”, Measurement: Sensors, 27, 100781, 2023.
[15]X. Li, Y. Lu, X. Fu, and Y. Qi, "Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing," Future Gener. Comput. Syst., vol. 118, pp. 282–296, 2021, doi: 10.1016/j.future.2021.01.016.
[16]B. Priyanka, V. M. Kalaivanan, R. A. Pavish, and M. Kanageshwaran, "IoT based pregnancy women health monitoring system for prenatal care," in 2021 7th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), Mar. 2021, pp. 1264–1269, doi: 10.1109/ICACCS51430.2021.9441677.
[17]R. Ivanov, S. Yordanov, and D. Dinev, "Internet of Things–based pregnancy tracking and monitoring service," in 2022 Int. Conf. Autom. Informatics (ICAI), Oct. 2022, pp. 298–302.
[18]H. Hashim, S. F. B. Salihudin, and P. S. M. Saad, "Development of IoT based healthcare monitoring system," in 2022 IEEE Int. Conf. Power Eng. Appl. (ICPEA), Mar. 2022, pp. 1–5.
[19]A. Velusamy, J. Akilandeswari, and M. Priya, "IoT-enabled intelligent maternal intensive care: A research study," in 2023 Int. Conf. Self Sustain. Artif. Intell. Syst. (ICSSAS), Oct. 2023, pp. 1377–1382.
[20]P. A. Mathina, S. Saravanadevi, and M. Pavithra, "Smart fetes monitoring system using healthcare IoT for pregnant women," in Proc. 2024 7th Int. Conf. Circuit Power Comput. Technol. (ICCPCT), vol. 1, Aug. 2024, pp. 607–612.
[21]J. Zhang, M. Lau, and Z. Zhu, “Hybrid CNN-GRU model for exercise classification using IMU time-series data,” J. Mach. Intell. Data Sci. (JMIDS), vol. 5, no. 1, pp. 54–64, 2024.
[22]A. Siddika and M. Sultana, “Maternal health risk analysis by using exploratory data analysis and machine learning algorithms,” Int. J. Eng. Res. Comput. Sci. Eng., vol. 10, pp. 146–151, 2023.
[23]A. Rattanasak, T. Jumphoo, W. Pathonsuwan, K. Kokkhunthod, K. Orkweha, K. Phapatanaburi, P. Tongdee, P. Nimkuntod, M. Uthansakul, and P. Uthansakul, “An IoT-enabled wearable device for fetal movement detection using accelerometer and gyroscope sensors,” *Sensors*, vol. 25, no. 5, Art. no. 1552, Mar. 2025, doi: 10.3390/s25051552.
[24]S. Veena, and D. J. Aravindhar, "Remote monitoring system for the detection of prenatal risk in a pregnant woman", Wireless Personal Communications, Vol. 119, no. 2, 1051-1064, 2021.
[25]M. Nazari, H. Sadr, H. Emami, and R. Rabiei, “A telemonitoring system for high-risk pregnancies: a novel approach to enhancing prenatal care and reducing maternal and fetal complications”, In 2025 11th international conference on web research (ICWR) (pp. 431-437). IEEE.
[26]S. Addanke and R. Anandan, "Secure IoT based smart system for monitoring health care for ambulatory and fetal," J. Auton. Intell., vol. 7, no. 3, 2024.
[27]B. Bala and S. Behal, S. “A brief survey of data preprocessing in machine learning and deep learning techniques”, In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 1755-1762), 2024. IEEE.
[28]R. Foorthuis, "On the nature and types of anomalies: A review of deviations in data," Int. J. Data Sci. Anal., vol. 12, no. 4, pp. 297–331, 2021, doi: 10.1007/s41060-021-00259-1.
[29]C. Bulut, and E. Arslan, E. "Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment", Artificial Intelligence Review, vol. 57, no. 9, 2024.