Enhancing Cybersecurity in IoT-Based Maternal Health Monitoring Systems Using Machine Learning Algorithms

The rapid expansion of IoT devices in maternal health monitoring enables continuous data collection and improved clinical assessment; however, it also introduces significant security and privacy concerns due to the sensitivity of maternal health information. This study investigates how artificial intelligence (AI) and machine learning (ML) can enhance both analytical performance and data protection in IoT-based maternal monitoring systems. The proposed framework employs Random Forest, Decision Tree, Support Vector Machine, and a stacking–bagging ensemble to improve maternal risk prediction and anomaly detection. Privacy-preserving techniques are integrated to secure physiological parameters: homomorphic encryption ensures data confidentiality during processing, while differential privacy limits information leakage from model outputs. Experimental results show that the stacking classifier combined with Random Forest achieved the highest accuracy of 82.3%, demonstrating greater robustness than traditional algorithms. Although differential privacy strengthened data protection, it reduced precision and F1-score, highlighting a trade-off between privacy and accuracy. Overall, integrating ensemble learning with privacy-preserving methods improves the security, accuracy, and reliability of IoT-driven maternal health monitoring systems.

[1] S. Sharma, H. Sidhu, and S. Kaur, “Analytical study of intrauterine fetal death cases and associated maternal conditions,” International Journal of Applied and Basic Medical Research, vol. 6, no. 1, pp. 11–13, 2016, doi: 10.4103/2229-516X.173986.

[2] K. Grym, H. Niela-Vilén, E. Ekholm, et al., “Feasibility of smart wristbands for continuous monitoring during pregnancy and one month after birth,” BMC Pregnancy and Childbirth, vol. 19, no. 34, pp. 1–9, 2019, doi: 10.1186/s12884-019-2187-9.

[3] M. G. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 365–374, 2003, doi: 10.1109/TBME.2003.808824.

[4] S. Bromuri, M. I. Schumacher, K. Stathis, and J. Ruiz, “Monitoring gestational diabetes mellitus with cognitive agents and agent environments,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Intell. Agent Technol., Lyon, France, 2011, pp. 409–414, doi: 10.1109/WI-IAT.2011.37.

[5] J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer Informatics, pp. 59–77, 2011, doi: 10.1177/1176935111415998.

[6] S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, and N. Qureshi, “Can machine-learning improve cardiovascular risk prediction using routine clinical data?” PLoS One, vol. 12, no. 4, 2017, doi: 10.1371/journal.pone.0174944.

[7] M. Ahmed, M. Abul Kashem, M. Rahman, and S. Khatun, “Review and analysis of risk factors of maternal health in remote areas using the Internet of Things (IoT),” in InECCE2019, 2020, pp. 357–365, doi: 10.1007/978-981-15-2317-5_30.

[8] M. Assaduzzaman, A. Al Mamun, and M. Hasan, “Early prediction of maternal health risk factors using machine learning techniques,” in 2023 International Conference for Advancement in Technology (ICONAT), Goa, India, 2023, doi: 10.1109/ICONAT57137.2023.10080700.

[9] A. Celdr?n, M. Gil Pérez, F. Garc?a Clemente, and G. Mart?nez Pérez, “Sustainable securing of medical cyber-physical systems for the healthcare of the future,” Sustainable Computing: Informatics and Systems, vol. 19, pp. 138–146, 2018, doi: 10.1016/j.suscom.2018.02.010.

[10] S. Pereira, R. Costa Filho, R. Ramos, and M. Oliveira, “Improving maternal risk analysis in public health systems,” in 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 2020, doi: 10.23919/SpliTech49282.2020.9243769.

[11] A. Raza, H. R. Siddiqui, K. Munir, and M. Almutairi, “Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction,” PLOS ONE, vol. 17, no. 11, p. e0276525, 2022, doi: 10.1371/journal.pone.0276525.

[12] P. Castillejo, J. F. Martinez, J. Rodriguez-Molina, and A. Cuerva, “Integration of wearable devices in a wireless sensor network for an e-health application,” IEEE Wireless Communications, vol. 20, no. 4, pp. 38–49, 2013, doi: 10.1109/MWC.2013.6590049.

[13] H.-Y. Chen, C.-H. Chuang, Y. J. Yang, and T. P. Wu, “Exploring the risk factors of preterm birth using data mining,” Expert Systems with Applications, vol. 38, no. 5, pp. 5384–5387, 2011, doi: 10.1016/j.eswa.2010.10.017.

[14] R. Gupta, A. Shukla, P. Mehta, and P. Bhattacharya, “VAHAK: A blockchain-based outdoor delivery scheme using UAV for Healthcare 4.0 services,” in Proc. IEEE INFOCOM WKSHPS, Toronto, ON, Canada, 2020, pp. 1–6, doi: 10.1109/INFOCOMWKSHPS50562.2020.9162738.

[15] A. Haleem, M. Javaid, R. Singh, and R. Suman, “Medical 4.0 technologies for healthcare: Features, capabilities, and applications,” Internet of Things and Cyber-Physical Systems, vol. 2, pp. 12–30, 2022, doi: 10.1016/j.iotcps.2022.04.001.

[16] T. M. Hussain, M. Shaikh, B. R. Ali, and H. Talpur, “Internet of Things as intimating for pregnant women’s healthcare: An impending privacy issue,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 12, no. 6, pp. 4337–4344, 2014, doi: 10.11591/telkomnika.v12i6.4227.

[17] R. Islam, M. Sayed, S. Saha, and M. J. Hossain, “Android malware classification using optimum feature selection and ensemble machine learning,” Internet of Things and Cyber Physical Systems, vol. 3, pp. 100–111, 2023, doi: 10.1016/j.iotcps.2023.03.001.

[18] A. Jaleel, T. Mahmood, M. A. Hassan, and G. Bano, “Towards medical data interoperability through collaboration of healthcare devices,” IEEE Access, vol. 8, pp. 132302–132319, 2020, doi: 10.1109/ACCESS.2020.3009783.

[19] M. Y. Oliveira, A. Pesqueira, M. Sousa, and F. Dal Mas, “The potential of big data research in healthcare for medical doctors’ learning,” Journal of Medical Systems, vol. 45, no. 13, 2021, doi: 10.1007/s10916-020-01691-7.

[20] Z. ElSayed, N. Elsayed, and S. Bay, “A novel zero-trust machine learning green architecture for healthcare IoT cybersecurity: Review, analysis, and implementation,” arXiv preprint, 2024, doi: 10.48550/arXiv.2401.07368.

[21] A. Gueriani, H. Kheddar, and A. Mazari, “Enhancing IoT security with CNN- and LSTM-based intrusion detection systems,” in Proc. Int. Conf. PAIS 2024, 2024, pp. xx–xx, doi: 10.1109/PAIS62114.2024.10541178.

[22] M. Akhi, S. F. Memon, C. Eising, and L. L. Dhirani, “Machine learning for healthcare-IoT security: A review and risk mitigation,” IEEE Access, pp. 1–1, 2023, doi: 10.1109/ACCESS.2023.3346320.

[23] T. Lai, F. Farid, A. Bello, and F. Sabrina, “Ensemble learning-based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis,” Cybersecurity, vol. 7, art. 44, 2024, doi: 10.1186/s42400-024-00144-x.

[24] S. T. Amin, S. Limkar, M. E. Abdelhag, M. H. Osman, et al., “An intelligent IoT framework for personalized healthcare: Integrating machine learning algorithms for real-time patient monitoring and diagnosis,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 16, pp. 702–712, 2024.

[25] M. H. Alsulami, “An AI-driven model to enhance sustainability for the detection of cyber threats in IoT environments,” Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 15, no. 5, 2024, doi: 10.14569/IJACSA.2024.0150506.

[26] F. ALmojel and S. Mishra, “Advancing hospital cybersecurity through an IoT-enabled neural network for human behavior analysis and anomaly detection,” Sensors, vol. 24, no. 22, p. 7179, 2024, doi: 10.3390/s24227179.

[27] Z. Pang, G. Yang, R. Khedri, and Y. T. Zhang, “Introduction to the Special Section: Convergence of automation technology, biomedical engineering, and health informatics toward Healthcare 4.0,” IEEE Rev. Biomed. Eng., vol. 11, pp. 249–259, 2018, doi: 10.1109/RBME.2018.2848518.

[28] L. Pawar, J. Malhotra, A. Sharma, and D. Arora, “A robust machine learning predictive model for maternal health risk,” in Proc. 3rd Int. Conf. Electron. Sustain. Commun. Syst. (ICESC), Coimbatore, India, 2022, doi: 10.1109/ICESC54411.2022.9885515.

[29] S. Pereira, F. Portela, and M. Santos, “Predicting type of delivery by identification of obstetric risk factors through data mining,” Procedia Comput. Sci., vol. 64, pp. 601–609, 2015, doi: 10.1016/j.procs.2015.08.573.