A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning

Title

A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning

Creator

Verma I.; Prasad S.K.

Description

Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide. 2023 IEEE.

Source

2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 615-624.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Child mortality; decision support; infants' health; machine learning; predictive modeling; risk assessment

Coverage

Verma I., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India, CHRIST (Deemed to be university) Delhi NCR, Ghaziabad, India; Prasad S.K., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India

Rights

Restricted Access

Relation

ISBN: 979-835030541-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/SmartTechCon57526.2023.10391399

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811405&doi=10.1109%2fSmartTechCon57526.2023.10391399&partnerID=40&md5=c2a75a52748fb1dbcc35e83a4f0c9370