Autism Spectrum Disorder Detection using Surface Morphometric Feature of sMRI in Machine Learning

Among various brain disorders, Autism Spectrum Disorder (ASD) is very different of its kind. It generally occurs at a very early age of children. It becomes difficult for even parents to identify an abnormality in their child due to its early occurrence. This paper presents the machine learning approach for the detection of ASD using surface morphometric features of T1 weighted structural Magnetic Resonance Imaging (sMRI). It also compares the classification evaluation of the utilized machine learning models based on left hemispheric surface and right hemispheric surface morphometric features of the brain. This work utilizes the Decision Tree (DT) and Random Forest (RF) for learning and classification purposes. Classification evaluation validates the better performance of RF in comparison to DT towards the classification between the controls and patients suffering from ASD.