Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning

Rajanikant Ghate, Sumiti Saharan, Rahee Walambe, Rahee Walambe

2023

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 insight on the socio-economic profile of users being reached and as an evaluation metric for interventions aimed at driving health equity.

Paper Citation

in Harvard Style

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) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 86-93. DOI: 10.5220/0011641700003414

in Bibtex Style

@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) - Volume 5: HEALTHINF},
year={2023},
pages={86-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011641700003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Predicting the Socio Economic Status of end Users of a Maternal Health App by Machine Learning
SN - 978-989-758-631-6
AU - Ghate R.
AU - Saharan S.
AU - Walambe R.
PY - 2023
SP - 86
EP - 93
DO - 10.5220/0011641700003414
PB - SciTePress