A Blockchain-based Model for Maternal Health Information Exchange and Prediction of Health Risks using Machine Learning

Proper antenatal and postnatal care are necessary to safeguard a woman's health as well as the health of her unborn child. The field of maternal healthcare is witnessing a dramatic shift as a result of recent advancements and new initiatives in medical technology. Still the issues of data security, reliability, transparency, informed decisions, and accountability in providing health care services to name a few. In order to address the aforementioned issues and challenges and reduce the rate of maternal mortality, adoption of medical technology will be essential. In this study, an attempt has been made to combine blockchain and predictive data analytics using an ensemble machine learning algorithm in order to collect, analyze, and store maternal health data securely and reliably and predict the risk level of pregnancy complications. The data stored on the blockchain cannot be altered; it serves as a secure setting for storing patient information as well as an authentic source for learning data. The publicly accessible dataset has been used to train and test the proposed blockchain-based pregnancy complications prediction system. The effectiveness of the proposed system has also been evaluated.