Researchers have developed an Artificial Intelligence (AI)-based system to predict the risk of early deaths due to chronic disease in middle-aged adults. The study, published by PLOS ONE journal, found that the new AI Machine Learning models ‘random forest’ and ‘deep learning’ were very accurate in its predictions and performed better than the current standard approach developed by human experts.
These new models take into account demographic, biometric, clinical and lifestyle factors for each person, and assess their consumption of fruit, vegetables and meat per day. The traditionally-used ‘Cox regression’ prediction model, based on age and gender, was found to be least accurate.
“Preventative healthcare is a growing priority in the fight against serious diseases so we have been working for a number of years to improve the accuracy of computerised health risk assessment in the general population,” says an expert. The study included over half a million people aged between 40 and 69.
Although these techniques could be new in health research and difficult to follow, clearly reporting these methods in a transparent way could help with scientific verification and future development of AI for health care.
