SVM-RBF based Maternal Healthcare Model
Keywords:
Decision support system, Machine learning, Maternal health, Predictive analytics, RBF, SVMAbstract
This paper presents a modeling based on Support Vector Machine (SVM) and Radial Basis Function (RBF) for the outcomes of maternal healthcare. This study proposes a machine learning–based predictive framework for maternal risk classification using a support vector machine with a radial basis function kernel. Maternal and child healthcare monitoring plays a critical role in reducing preventable morbidity and mortality. A structured dataset containing 50 clinical and demographic attributes was processed through systematic feature selection, normalization, and stratified sampling. The model was evaluated using a 70:30 train-test split and 5-fold cross-validation. Experimental results demonstrate an accuracy of 87%, with strong sensitivity and ROC performance. The predictive model was deployed via a web-based interface using Streamlit, enabling real-time maternal health risk assessment. The findings confirm the applicability of kernel-based learning in healthcare analytics. Future work includes ensemble modeling and explainable AI integration to improve interpretability and generalizability. The nutritional and hematological condition of individuals is indicated through records of Iron and Folic Acid (IFA) supplementation as well as hemoglobin levels, both of which are essential factors in determining maternal anemia and the overall risks associated with pregnancy. Furthermore, the dataset includes structured indicators of high-risk situations to capture significant clinical warning signs noted during prenatal evaluations. The outcome variable is defined as a binary class, indicating whether high-risk pregnancy status is present or absent. This organized blend of demographic, clinical, and preventive healthcare factors facilitates the creation of a strong predictive model for classifying maternal risk while ensuring a thorough representation of features for supervised learning analysis. The selected feature scaling was executed using standardization to normalize the distribution of numerical variables. Because Support Vector Machines are affected by the magnitude of features, standardization ensured that each attribute contributed equally to the formation of the decision boundary, subsequently enhancing convergence stability and predictive accuracy.
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