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Development and Validation of Prediction Model for Neonatal Intensive Care Unit (NICU) Admission Using Machine Learning and Multivariate Statistical Approach

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Abstract

Background

The work aims to provide a method for assessing newborn infants' risk to determine whether they need to be admitted to the NICU before birth. Term neonates who were admitted to the NICU are known to have health problems, a greater mortality risk, and higher healthcare costs. Researchers want to develop an algorithm that estimates the risk of NICU admission for this particular subset of newborns using an integrated statistical approach. This risk assessment might lower the morbidity, mortality, and healthcare expenses related to NICU hospitalizations by assisting in the early identification of possible instances.

Method

The data was collected from a multispecialty hospital using hospital-based records from the obstetrics and gynecology department. All the clinical and demographic parameters are described as per requirement. A multivariate statistical analysis was done to identify potential risk factors for NICU admission. Four classification models were used to predict NICU admission. All the models were evaluated based on their performance matrices.

Results

In multivariate analysis, we found Preterm deliveries (β = 1.003 Aor = 2.727 95% CI = 1.54,4.80 P < 0.001), Hypertension (β = − 1.419 Aor = 0.242 95% CI = 0.112,0.523 P < 0.001), AFI (β = 1.262 Aor = 3.53 95% CI = 1.06,11.69 P = 0.039), Birth weight (< 2.5 kg) (β = 1.011 Aor = 2.75 95% CI = 1.57,4.81 P < 0.001), Mode of Delivery(LSCS)(β = 1.196 Aor = 3.307 95% CI = 1.95,5.60 P < 0.001) and maternal complication (β = 6.962 OR = 7.69 95% CI = 5.67,13.69 P < 0.001) are the potential risk factors for NICU admission. The decision tree performed the highest accuracy (0.921) and AUC (0.966) as compared to other models to predict NICU admission.

Conclusion

Using an explainable feature learning technique to predict NICU admissions contributes to better global health data utilization and a more hopeful future in healthcare.

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Abbreviations

RF:

Random forest

KNN:

K nearest neighbor

LR:

Logistic regression

AFI:

Amniotic fluid index

SVM:

Support vector machine

NICU:

Neonatal intensive care unit

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Acknowledgements

We wish to acknowledge the study participant and the Department of Obstetrics and Gynecology for the necessary help & support for data collection and research.

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No funding provided for this research.

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Authors and Affiliations

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

    Nihar Ranjan Panda & Kamal Lochan Mahanta

  2. KIIT International School, KIIT University, Bhubaneswar, India

    Jitendra Kumar Pati

  3. Department of Obstetrics and Gynecology, IMS & SUM Hospital, Soa, Deemed to Be a University, Bhubaneswar, India

    Tapasi Pati

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  1. Nihar Ranjan Panda
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Correspondence to Nihar Ranjan Panda.

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Ethical Approval

The data were collected using standard treatment protocols, and hence, there was no direct risk to the participants. This study was approved by the Institutional Ethics Committee.

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Informed consent was obtained from all individual participants involved in the study.

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This research was carried out in accordance with the Helsinki Declaration's standards.

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Panda, N.R., Mahanta, K.L., Pati, J.K. et al. Development and Validation of Prediction Model for Neonatal Intensive Care Unit (NICU) Admission Using Machine Learning and Multivariate Statistical Approach. J Obstet Gynecol India (2024). https://doi.org/10.1007/s13224-024-02009-0

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