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Dr Dweepobotee Brahma, assistant professor, Centre for Mathematical and Computational Economics, School of AI and Data Science, IIT Jodhpur, and Dr Debasri Mukherjee, professor, Department of Economics, Western Michigan University, USA, have developed an early warning system for neonatal and infant mortality using ML techniques. The study uses nationwide household survey data from India. The main objective of this research was to identify early warning signs of child mortality that community health workers can use.
“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,” said Dr Dweepobotee Brahma.
The early-warning indicators include:
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- Observable biological characteristics
- Demographic characteristics
- Socio-economic factors of households, mothers and newborns
The early warning indicators identified in this study do not require advanced medical knowledge and can be easily used by community healthcare workers. The study uses a range of machine learning algorithms to assess the relative importance of characteristics, like being first-borns, being born in poorer households, and having a low birth weight.
The research team said that they aim 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. Early identification of risk factors will allow women and newborns to get timely medical care, which will reduce the child mortality rate in India.
You can access the research here.