Machine Learning can Change Patient Care in Nigeria for Good

For decades, 97% of the data in these sources was unused, trapped in stacks of paper. In 2030, the Nigerian healthcare system can deliver proactive, predictive healthcare that is widely accessible and affordable. Pattern recognition algorithms that help detect hepatitis B virus in vulnerable Nigerian populations are a step in this direction. Nigeria’s rapidly growing population is overstretching the understaffed and underfunded healthcare system. But machine learning and predictive analytics could help in three areas: expanding access, improving quality of care, and reducing costs. For Nigeria and similar countries, hepatitis B virus provides an example. Hepatitis B is a leading cause of chronic liver disease and death worldwide. At least 1 in 10 Nigerians live with viral hepatitis B, translating into about 20 million infected people. Yet they are missing from the global public health agenda because of the cost and other limitations of diagnostic tests. Most people living with this silent killer are unaware of their infection status, and at risk of transmitting the virus to others.


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