A Medical Cyber-physical system for predicting maternal health in developing countries using machine learning

Abstract

It is essential to monitor any health issues during pregnancy to ensure a safe delivery because pregnancy is crucial for both mother and child. However, developing countries have poor access to healthcare, making managing possible health risks during pregnancy challenging. An Internet of Things (IoT)-based Medical Cyber-Physical System (MCPS) can offer a valuable and affordable solution for anticipating and controlling health hazards during pregnancy to solve this issue. This paper presents the design and development of an MCPS for recognizing health risks in pregnant women in developing countries. The system collects key health metrics using temperature, blood pressure, glucose levels, and heart rate sensors. It automatically considers risk factors to predict health risks using Machine Learning (ML) and sends them to the nearest clinic or hospital. Patients can manually enter their risk factors into the program and talk with a doctor through it. The efficacy of the proposed MCPS is evaluated using a dataset of pregnant women, and the results demonstrate that the system can accurately detect health issues during pregnancy. Medical experts can.

enhance maternal and fetal health outcomes using the systems real-time data collecting and processing capabilities. Despite restricted access to healthcare in developing countries, the proposed MCPS provides a valuable and economical method of addressing pregnancy-related health risks. The MCPS can assist medical personnel in making quick and informed choices, enhancing the level of care provided to expectant mothers and their unborn children.