An Intelligent Machine Learning Framework for Neonatal Health Risk Prediction and Early Clinical Decision Support

Naresh Kumar P; Shruti S; Surya C; Mekala M

An Intelligent Machine Learning Framework for Neonatal Health Risk Prediction and Early Clinical Decision Support

Author(s)	Prof. Naresh Kumar P, Ms. Shruti S, Ms. Surya C, Ms. Mekala M
Country	India
Abstract	Neonatal Jaundice is among the most frequently diagnosed health issues for infants and could cause serious issues, such as Kernicterus (brain damage from accumulation of blood products related to jaundice) if the condition is not promptly diagnosed. This study describes the development of an Artificial Intelligence (AI) based Jaundice Prediction System that predicts the risk of developing Neonatal Jaundice based on specific clinical characteristics. The system utilizes Machine Learning algorithms to predict how likely a child is to develop Neonatal Jaundice based on a variety of health information, including: Gestational Age, Birth Weight, Bilirubin levels, feeding frequency, oxygen saturation, body temperature, and other clinical chart data. Using the Random Forest Machine Learning algorithm, the system classifies the risks associated with Neonatal Jaundice into one of four categories (low, moderate, high, and critical risk). In building the system, an interactive user interface was developed using Stream lit, the Random Forest model was built using Scikit-Learn, and Plotly was used for data visualization, resulting in a very user-friendly interactive healthcare dashboard. The prediction system also provides AI-generated insights, feature importance analysis, and medical recommendations in order to support the healthcare provider with making decisions related to patients at risk for developing Neonatal Jaundice. The system retains patient history, performs risk trend analysis, and produces performance metrics of the predictive model to support continuous monitoring of the patient. Testing has demonstrated that the Random Forest Model used to create the Jaundice Prediction System demonstrates excellent accuracy and reliability in predicting the risk of Neonatal Jaundice.
Keywords	NEONATAL JAUNDICE PREDICTION, MACHINE LEARNING, RANDOM FOREST CLASSIFICATION
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-14

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An Intelligent Machine Learning Framework for Neonatal Health Risk Prediction and Early Clinical Decision Support

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