Child malnutrition in Kenya: AI model can forecast rates six months before they become critical

Globally, nearly half of the deaths of children under five years are linked to malnutrition. In Kenya, it’s the leading cause of illness and death among children.

Children with malnutrition typically show signs of recent and severe weight loss. They may also have swollen ankles and feet. Acute malnutrition among children is usually the result of eating insufficient food or having infectious diseases, especially diarrhoea.

Acute malnutrition weakens a child’s immune system. This can lead to increased susceptibility to infectious diseases like pneumonia. It can also cause more severe illness and an increased risk of death.

Currently, the Kenyan national response to malnutrition, implemented by the ministry of health, is based on historical trends of malnutrition. This means that if cases of malnutrition have been reported in a certain month, the ministry anticipates a repeat during a similar month in subsequent years. Currently, no statistical modelling guides responses, which has limited their accuracy.

The health ministry has collected monthly data on nutrition-related indicators and other health conditions for many years.

Our multi-disciplinary team set out to explore whether we could use this data to help forecast where, geographically, child malnutrition was likely to occur in the near future. We were aiming for a more accurate forecast than the existing method.

We developed a machine learning model to forecast acute malnutrition among children in Kenya. A machine learning model is a type of mathematical model that, once “trained” on an existing data set, can make predictions of future outcomes. We used existing data and improved forecasting capabilities by including complementary data sources, such as satellite imagery that provides an indicator of crop health.

We found that machine learning-based models consistently outperformed existing platforms used to forecast malnutrition rates in Kenya. And we found that models with satellite-based features worked even better.

Our results demonstrate the ability of machine learning models to more accurately forecast malnutrition in Kenya up to six months ahead of time from a variety of indicators.

If we have advance knowledge of where malnutrition is likely to be high, scarce resources can be allocated to these high-risk areas in a timely manner to try to prevent children from becoming malnourished.

How we did it

We used clinical data from the Kenya Health Information System. This included data on diarrhoea treatment and low birth weight. We collected data on children who visited a health facility who met the definition of being acutely malnourished, among other relevant clinical indicators.

Given that food insecurity is a key driver of acute malnutrition, we also incorporated data reflecting crop activity into our models. We used a NASA satellite to look at gross primary productivity, which measures the rate at which plants convert solar energy into chemical energy. This provides a coarse indicator of crop health and productivity. Lower average rates can be an early indication of food scarcity.

We tested several methods and models for forecasting malnutrition risk among children in Kenya using data collected from January 2019 to February 2024.

The gradient boosting machine learning model – trained on previous acute malnutrition outcomes and gross primary productivity measurements – turned out to be the most effective model for forecasting acute malnutrition among children.

This model can forecast where and at what prevalence level acute malnutrition among children is likely to occur in one month’s time with 89 per cent accuracy.

All the models we developed performed well where the prevalence of acute child malnutrition was expected to be at more than 30 per cent, for instance in northern and eastern Kenya, which have dry climates. However, when the prevalence was less than 15 per cent, for instance in western and central Kenya, only the machine learning models were able to forecast with good accuracy.

This higher accuracy is achieved because the models use additional information on multiple clinical factors. They can, therefore, find more complex relationships.

Implications

Current efforts to predict acute malnutrition among children rely only on historical knowledge of malnutrition patterns. We found these forecasts were less accurate than our models.

Our models leverage historical malnutrition patterns, as well as clinical indicators and satellite-based indicators.

The forecasting performance of our models is also better than other similar data-based modelling efforts published by other researchers.

As resources for health and nutrition shrink, improved targeting to the areas of highest need is critical. Treating acute malnutrition can save a child’s life.

Prevention of malnutrition promotes children’s full psychological and physical development.

What needs to happen next

Making these data from diverse sources available through a dashboard could inform decision-making. Responders could get six months to intervene where they are most needed.

We have developed a prototype dashboard to create visualisations of what responders would be able to see based on our model’s subcounty-level forecasts. We are currently working with the Kenyan ministry of health and Amref Health Africa, a health development NGO, to ensure that the dashboard is available to local decision-makers and stakeholders. It is regularly updated with the most current data and new forecasts.

We are also working with our partners to refine the dashboard to meet the needs of the end users and promote its use in national decision-making on responses to acute malnutrition among children. We’re tracking the impacts of this work.

Throughout this process, it will be important to strengthen the capacity of our partners to manage, update and use the model and dashboard. This will promote local responsiveness, ownership and sustainability.

Scaling up

The Kenya Health Information System relies on the District Health Information System 2 (DHIS2). This is an open source software platform. It is currently used by over 80 low- and middle-income countries. The satellite data that we used in our models is also available in all of these countries.

If we can secure additional funding, we plan to expand our work geographically and to other areas of health. We’ve also made our code publicly available, which allows anyone to use it and replicate our work in other countries where child malnutrition is a public health challenge.

Furthermore, our model proves that DHIS2 data, despite challenges with its completeness and quality, can be used in machine learning models to inform public health responses. This work could be adapted to address public health issues beyond malnutrition, like changes in patterns of infectious diseases due to climate change.

This work was a collaboration between the University of Southern California’s Institute on Inequalities in Global Health and Center for Artificial Intelligence in Society, Microsoft, Amref Health Africa and the Kenyan ministry of health.