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Authors: Rajanikant Ghate 1 ; Sumiti Saharan 1 and Rahee Walambe 1 ; 2

Affiliations: 1 Avegen Ltd, London, U.K. ; 2 Symbiosis Institute of Technology (SIT), Symbiosis Centre for Applied AI (SCAAI), Symbiosis International University, Pune, India

Keyword(s): Socio-Economics Status (SES), Impact Analysis, Maternal Health, Machine Learning.

Abstract: Digital technologies posit an immense opportunity to provide scalable solutions for narrowing the health equity gap and proving affordable access to quality healthcare in low resource settings. A key step towards harnessing the power of digital health is developing a scalable mechanism for identifying the socioeconomic profile of end users. Socio-economic status (SES) of individuals has been classically estimated through standard questionnaires. This methodology is not scalable and prone to immense bias if implemented digitally as a self-report questionnaire. Together for Her (TFH) is a digital app for pregnancy that aims to provide equitable access to quality pregnancy information and support to pregnant women in India. To assess our reach to users from low socio-economic settings, we developed a machine learning model that leverages digital indices for estimated SES. We propose this approach holds immense value for digital health interventions, both as a mechanism for gaining insig ht on the socio-economic profile of users being reached and as an evaluation metric for interventions aimed at driving health equity. (More)

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Paper citation in several formats:
Ghate, R.; Saharan, S. and Walambe, R. (2023). Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - HEALTHINF; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 86-93. DOI: 10.5220/0011641700003414

@conference{healthinf23,
author={Rajanikant Ghate. and Sumiti Saharan. and Rahee Walambe.},
title={Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - HEALTHINF},
year={2023},
pages={86-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011641700003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - HEALTHINF
TI - Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning
SN - 978-989-758-631-6
IS - 2184-4305
AU - Ghate, R.
AU - Saharan, S.
AU - Walambe, R.
PY - 2023
SP - 86
EP - 93
DO - 10.5220/0011641700003414
PB - SciTePress

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