Authors: S. Gouthami, A. Srilekha
Abstract: Low birth weight (LBW), defined as a birth weight of less than 2500 grams, is one of the leading indicators of neonatal morbidity, infant death, and long-term developmental difficulties. Healthcare professionals should be able to detect pregnancies at risk of delivering infants with low birth weights early on, which might improve maternal and newborn outcomes. Traditional risk assessment methods rely heavily on maternal history and clinical findings, but they may not always provide reliable predictions. An artificial intelligence (AI) framework for the early diagnosis and classification of Low Birth Weight is presented in this research, which makes use of demographic, clinical, and prenatal healthcare data for mothers. The proposed system uses a variety of supervised machine learning algorithms for data collection, preprocessing, feature selection, model training, and performance comparison evaluation. These algorithms include Support Vector Classifier (SVC), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost). The dataset is prepared for training prediction models by performing operations such handling missing values, eliminating outliers, decreasing noise, and optimising features. To determine the most effective prediction model, experimental evaluations are conducted on Accuracy, Precision, Recall, and F1-Score. The Random Forest Classifier has the highest prediction accuracy among the evaluated classifiers, at around 84.81%. A web app developed on Django allows medical professionals to enter maternal health data. The program employs an internal prediction engine to generate real-time risk estimates for low birth weight. The user interface is easy and engaging. A more effective decision-support tool within the proposed framework may lead to better prenatal healthcare planning, earlier diagnosis, and fewer issues with low birth weight in newborns.
DOI: http://doi.org/10.5281/zenodo.21375077
