Behavioral Diagnosis of Children Utilizing Support Vector Machine for Early Disorder Detection

Abstract

Behavioral issues are categorized by means of persistent difficulties faced from the beginning of childhood. The children with this behavioral disorder restrict social communication and show repetitive interest. Some children have challenging behaviors that are beyond their age and identification becomes difficult. These problems can cause temporary stress on a child's health. A lot of children are impacted by behavioral-related issues from birth, and unfortunately, no scientifically backed early detection mechanism is available to identify the stated issues within the first three years. Using AI and data analytics algorithm, the behavioral profile of a child can be analyzed using a key marker to identify behavioral issues later. Cloud-based AI solutions could be used to implement early detection. Machine learning algorithms using SVM have the potential to create the decision boundary for the segregation using possible classes.