Indian Institute of Technology (IIT) Jodhpur and Western Michigan University, USA in collaboration have developed an early warning system for neonatal and infant mortality predictors using multiple machine learning (ML) techniques. The study has used nationwide household survey data from India. The primary objective of the research was to identify early warning signs of child mortality that community health workers can use.
The research aims to train community health workers to use predictors as a screening mechanism to identify individuals at risk for mortality and refer them to qualified doctors for more rigorous evaluation. The early-warning indicators include observable biological characteristics, demographic characteristics, socio-economic factors of households, mothers and new-borns.
The study claims to use a range of machine learning algorithms to assess the relative importance of characteristics such as being first-borns, being born in poorer households, and having a low birth weight. The early warning indicators identified in the study do not require advanced medical knowledge and can be easily used by community healthcare workers.
“Early identification of risk factors through the help of community health workers can go a long way in helping India reach the Sustainable Development Goals,” Dweepobotee Brahma, assistant professor, Centre for Mathematical and Computational Economics, School of AI and Data Science, IIT Jodhpur, said.
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