Artificial Intelligence to predict future diabetes cases with 94 per cent accuracy
Researchers have revealed that with the help of artificial intelligence (AI) their trained computer model predicted the future incidence of diabetes with an overall accuracy of 94.9 per cent. Artificial intelligence (AI) is the development of computer systems able to perform tasks that normally require human intelligence.
Diabetes is linked to increased risks of severe health problems, including heart disease and cancer. Preventing diabetes is essential to reduce the risk of illness and death.
"Currently, we do not have sufficient methods for predicting which generally healthy individuals will develop diabetes," said study lead author Akihiro Nomura from Kanazawa University in Japan.
"Using machine learning, it could be possible to precisely identify high-risk groups of future diabetes patients better than using existing risk scores," Nomura added.
For the findings, published in the Journal of the Endocrine Society, the researchers investigated the use of a type of artificial intelligence called machine learning in diagnosing diabetes.
Machine learning is a type of AI that enables computers to learn without being explicitly programmed.
The research team analysed 509,153 nationwide annual health checkup records from 139,225 participants from 2008 to 2018 in the city of Kanazawa in Japan.
Among them, 65,505 participants without diabetes were included. The data included physical exams, blood and urine tests and participant questionnaires.
Patients without diabetes at the beginning of the study who underwent more than two annual health checkups during this period were included.
New cases of diabetes were recorded during patients' checkups, the researchers said.
The researchers identified a total of 4,696 new diabetes patients (7.2 per cent) in the study period. Their computer model predicted the future incidence of diabetes with an overall accuracy of 94.9 per cent.
According to the authors, the next plan is to perform clinical trials to assess the effectiveness of using statins to treat groups of patients identified by the machine learning model as being at high risk of developing diabetes.
