A Machine Learning Approach for Early Detection of Learning Disorders in Pediatrics

Learning disorders are common among Pediatric individuals with Autism Spectrum Disorder(ASD). Although autism itself is not a learning disability, it can significantly affect a child's ability to process and retain information. Early diagnosis of Pediatric individuals who may have chances of developing learning disorder might help to by a lot provide effective treatment. This research aims to develop mechanisms to detect learning disorder in Pediatric individuals aged from 1 to 18 years old. Eight machine learning algorithms were tested to curate an accurate model to predict whether a pediatric individual is susceptible to developing learning disorder or not. Random Forest outperformed all the classifiers. The model was calibrated by amalgamating a tool that enables the model to interpret and retrace the underlying factors that contribute to the results. The experimental results revealed that it is possible to detect learning disorder in Pediatric individuals with high reliability.