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Explainable AI Enabled Infant Mortality Prediction Based on Neonatal Sepsis

Priti Shaw1, Kaustubh Pachpor2, Suresh Sankaranarayanan3,*
1 Barclays Bank, Bund Garden Road, Pune, 411001, India
2 University of Illinois, 60607, Illinois, USA
3 SRM Institute of Science and Technology, Chennai, 603203, India
* Corresponding Author: Suresh Sankaranarayanan. Email:

Computer Systems Science and Engineering 2023, 44(1), 311-325. https://doi.org/10.32604/csse.2023.025281

Received 18 November 2021; Accepted 07 January 2022; Issue published 01 June 2022

Abstract

Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem, especially in developing countries. There have been researches on human sepsis, vaccine response, and immunity. Also, machine learning methodologies were used for predicting infant mortality based on certain features like age, birth weight, gestational weeks, and Appearance, Pulse, Grimace, Activity and Respiration (APGAR) score. Sepsis, which is considered the most determining condition towards infant mortality, has never been considered for mortality prediction. So, we have deployed a deep neural model which is the state of art and performed a comparative analysis of machine learning models to predict the mortality among infants based on the most important features including sepsis. Also, for assessing the prediction reliability of deep neural model which is a black box, Explainable AI models like Dalex and Lime have been deployed. This would help any non-technical personnel like doctors and practitioners to understand and accordingly make decisions.

Keywords

APGAR; sepsis; explainable AI; machine learning

Cite This Article

P. Shaw, K. Pachpor and S. Sankaranarayanan, "Explainable ai enabled infant mortality prediction based on neonatal sepsis," Computer Systems Science and Engineering, vol. 44, no.1, pp. 311–325, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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