Machine learning can solve some of Africa’s biggest challenges

Machine learning democratises access to innovative and productivity-boosting technology to fuel growth the continent needs.

From better healthcare access to improved food security, machine learning could tackle a wide range of challenges in developing countries.

In 2020, a study published in Nature showed that Google’s machine learning artificial intelligence programme, DeepMind AI, outperformed radiologists in detecting breast cancer.

After being trained on thousands of mammograms, the system was able to accurately identify 89 percent of breast cancer cases, compared to radiologists’ 74 percent. Just imagine what a difference the deployment of such a system could make in sub-Saharan Africa, where there are 0.2 doctors per 1,000 people, according to the World Bank.

And that’s just the start. Marilyn Moodley, country leader for South Africa and WECA (West, East, Central Africa) at SoftwareONE, says machine learning can help with some of the region’s most pervasive problems, from reducing poverty and improving education to delivering healthcare and addressing sustainability challenges such as food demand.

“Machine learning democratises access to innovative and productivity-boosting technology to fuel the growth the continent needs. It’s fundamentally reshaping how work is done, allowing for a more efficient allocation of resources leading to increased productivity and, in the case of the government, improving the delivery of services to citizens,” she says.

Agricultural improvements
The agriculture sector employs more than 65 percent of Africa’s labour force and accounts for 32 percent of gross domestic product (GDP), says Marilyn.

“The World Bank estimates that African food markets will be worth US$1 trillion by 2030, up from the current $300 billion. Demand for food is projected to at least double by 2050, yet the sector is burdened with limitations. Land is degrading, soil is becoming less fertile, water tables are dropping, pests are becoming more resistant, and the climate is more vulnerable and unpredictable. All this could have disastrous effects on food availability in the future.”

Machine learning has the potential to improve productivity and efficiency at all stages of the agricultural value chain, she says.

“These technologies can empower small-holder farmers to increase their income through higher crop yield and greater price control,” she explains. “For example, analytics of crop data can help identify diseases, enable soil health monitoring without the need for laboratory testing infrastructure, and facilitate the creation of virtual co-operatives to aggregate crop yields and broker better prices with suppliers.”

Healthcare developments
Machine learning can not only analyse tests and images to suggest diagnoses, but also aggregate data and update patients’ charts. It’s also rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research, adds Marilyn.

“This would free up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting their doctors with artificial intelligence to combat their doctor shortage.”

In Africa, machine learning could plug the same gap, but also address other challenges, she explains. “Health systems in Africa face several structural challenges such as shortages of qualified professionals or supplies, resulting in divergent outcomes for patients. Even when facilities and staff are available, affordability and rural/urban disparities can put needed services out of reach of patients.”

Machine learning can enhance these outcomes in the following ways, she highlights:
Improve healthcare delivery: Advanced data analytics can help practitioners identify potential problems early and tailor better preventive care. Early interventions make healthcare more affordable and easier for the patient, with better outcomes.

Better diagnostics and detection: Analysing patterns in data, such as machine vision analysis of X-rays, can make diagnoses faster and more accurate.
Improved access: Tools such as online conversation agents can extend access to millions of people and remotely diagnose various health conditions using images from the cameras of everyday smartphones.

Marilyn concludes that, “Machine learning is a powerful tool that can benefit multiple industries, including marketing, financial services, transportation and manufacturing. Possible use cases are boundless and clearly demonstrate the importance of innovative technology for ensuring efficient business processes.”