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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Development and Validation of Prediction Model for Neonatal Intensive Care Unit (NICU) Admission Using Machine

Nihar Ranjan Panda1, Kamal Lochan Mahanta1, Jitendra Kumar Pati2

  • 1Department of Mathematics, CV Raman Global University, Bhubaneswar, India.

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|May 20, 2025
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This study developed a predictive algorithm to identify newborns needing Neonatal Intensive Care Unit (NICU) admission. The model aids early intervention, potentially reducing infant mortality and healthcare costs.

Keywords:
ClassificationMultivariate analysisNICU admissionPrediction model

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Area of Science:

  • Neonatal Health
  • Medical Informatics
  • Predictive Analytics

Background:

  • Neonatal Intensive Care Unit (NICU) admissions are associated with increased mortality and healthcare costs.
  • Identifying at-risk neonates before birth is crucial for timely intervention.
  • Current methods for risk assessment require enhancement for improved accuracy.

Purpose of the Study:

  • To develop and validate a predictive algorithm for estimating the risk of NICU admission in newborns.
  • To identify key clinical and demographic factors associated with NICU admission.
  • To improve early identification of neonates requiring intensive care.

Main Methods:

  • Utilized hospital-based obstetric and gynecology records for data collection.
  • Performed multivariate statistical analysis to identify risk factors for NICU admission.
  • Developed and evaluated four classification models, including decision trees, to predict NICU admission.

Main Results:

  • Identified preterm delivery, hypertension, AFI, low birth weight (<2.5 kg), Cesarean section (LSCS), and maternal complications as significant risk factors for NICU admission.
  • The decision tree model demonstrated the highest predictive accuracy (0.921) and AUC (0.966).

Conclusions:

  • An explainable feature learning technique can effectively predict NICU admissions.
  • This approach enhances global health data utilization for improved neonatal care.
  • Predictive modeling offers a promising avenue for reducing neonatal morbidity, mortality, and healthcare expenses.