A new machine learning model can predict autism in young children from relatively limited information. This is shown in a new study by Karolinska Institutet published in . The model can facilitate early detection of autism, which is important to provide the right support.
"With an accuracy of almost 80% for children under the age of two, we hope that this will be a valuable tool for health care," says Kristiina Tammimies, Associate Professor at KIND, the Department of Women's and Children's Health, Karolinska Institutet and last author of the study. The research team used a large US database (SPARK) with on approximately 30,000 individuals with and without spectrum disorders. By analyzing a combination of 28 different parameters, the researchers developed four distinct machine-learning models to identify patterns in the data.
The parameters selected were information about children that can be obtained without extensive assessments and medical tests before 24 months of age. The best-performing model was named "AutMedAI." Among about 12,000 individuals, the AutMedAI model was able to identify about 80% of children with autism.
In specific combination with other parameters, age of first smile, first short sentence and the presence of eating difficulties were strong predictors of autism. "The results of the study are significant because they show that it is possible to identify individuals who are likely to have autism from relatively limited and readily available information," s.