Archana Maju
Rajkumari Amrit Kaur College of Nursing Delhi, India
Abstract Title:
Empowering Prenatal Care through Nursing Innovation: An AI-Based Predictive Model for Low Birth Weight
Biography:
A dedicated teaching faculty with over 15 years of teaching experience at RAK College of Nursing, specializing in clinical instruction, curriculum development, and research mentoring. She has published in Scopus-indexed journals, serves as a reviewer for reputed publications, and is a certified Master Trainer for NELS. Recognized for academic and research excellence, she has received multiple awards including Best Scientific Paper(IANN), Best Scientific Poster, and the Academic Excellence Award in MSc Nursing.
Research Interests:
Background: Low birth weight serves as a vital measure of maternal health and the efficacy of prenatal care globally. The study was aimed to assess the incidence and risk factors of Low-birth-weight among neonates. Further to develop a predictive model that identifies the risk factors contributing to low birth weight using Artificial intelligence.
Methods: The study employed a dual research design, incorporating both descriptive and case-control methodologies. The data was analysed using descriptive and inferential statistics. Further a predictive model was developed using logistic regression through artificial intelligence.
Results: The incidence rate of low-birth-weight babies was approximately 304.7 (30.47%) per 1000 live births. Logistic regression analysis identified significant risk factors for low birth weight (LBW), with notably high adjusted odds ratios (AOR). Key factors included inadequate weight gain during pregnancy <9 kg (AOR = 11.89, 95% CI: 6.03–23.44), gestational age <37 weeks (AOR = 12.81, 95% CI: 6.55–25.02), fetal complications reported during pregnancy (AOR = 13.25, 95% CI: 6.81–25.77), and multiple gestation (AOR = 26.88, 95% CI: 3.31–217.99). The developed AI-enabled predictive model demonstrates a high overall accuracy of 90%.
Conclusion: Most identified risk factors are modifiable, and early prenatal care can greatly reduce LBW incidence and improve neonatal outcomes. The predictive model demonstrated strong accuracy in classifying newborns by birth weight. Integrating the model into healthcare systems can aid early risk detection, reducing low birth weight and improving neonatal outcomes.
Key words: Incidence, Low birth weight, predictive model, risk factors.
