A machine learning approach can predict risk factors for children’s behavioral disorders: A study

A study published in ‘Biological Psychiatry: Cognitive Neurology and Neurovisual’ stated that machine learning could assess risk factors and predict the further development of behavioral disorders (CD) in children with high accuracy.

Behavioral disorders are considered to be a serious behavioral and emotional disorder that can occur in children and adolescents.

Experts noted that the factors that contribute to CD development include the biological, psychological, and social spheres.

In the study, the researchers identified risk factors that could help predict CD.

The data were evaluated after lengthy study following the biopsychosocial development of the children.

The researchers used baseline data from more than 2,300 children aged 9 to 10 years enrolled in the Adolescent Cognitive Developmental Study (ABCD).

To complete the study, the researchers “studied” their machine learning model using predefined risk factors across different biopsychosocial domains.

The model predicted CD development two years later with more than 90% accuracy.

The ability to accurately predict who can develop a CD will help researchers and healthcare professionals in developing programs for at-risk youth with the potential to reduce the harmful effects of CDs on children and their families.

Cameron Carter, MD, editor of Biopsy Psychiatry: Neuroscience Cognitive and Neuroimaging, said: “These remarkable results using function-based functional MRI to study the function of the reward system show that the risk of subsequent depression in children of depressed mothers is more dependent on the mother. . responding to their children’s emotional behavior toward the mother’s spirit. ”

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(With information from agencies)

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