Maternal health risk prediction involves developing models or systems that can assess the risk of complications or adverse outcomes during pregnancy and childbirth for expecting mothers. The objective is to identify high-risk individuals early on, enabling healthcare providers to intervene with appropriate care and interventions to improve maternal and fetal health outcomes.
The tech stack used in this project involve a combination of programming languages (such as Python) and libraries (such as scikit-learn, Pandas) for data processing, analysis, and machine learning.
The project utilizes statistical modeling techniques, such as logistic regression or decision trees, as well as advanced machine learning algorithms like random forests, support vector machines, or deep learning models.
The methodology typically involves data collection from various sources (e.g., electronic health records, surveys), data preprocessing, feature engineering, model training, and evaluation. Cross-validation and performance metrics like accuracy are used to assess the predictive performance of the models. The project follows an iterative approach, refining the models based on feedback and continuously improving the prediction accuracy.
Maternal health risk prediction aims to reduce maternal mortality, maternal morbidity, and improve overall maternal health by enabling early identification and management of high-risk pregnancies. By providing healthcare providers with valuable insights, it supports informed decision-making and ensures that appropriate care is delivered to those who need it most, thus safeguarding the well-being of both mothers and their babies.