Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE)

The autism diagnostic interview-revised (ADI-R) is a semi-structured interview designed to assess the three core aspects of autism spectrum disorder (ASD). In this research a synthetic minority over-sampling technique (SMOT) was presented for handling autism imbalanced data to increase accuracy credibility. SMOT can potentially lead to over fitting on multiple copies of minority class examples. The autism data collected from National Research Center in Egypt (NRC). The experimental dataset applied on several machine learning algorithms and compared the accuracy before and after over-sampling techniques. The result show that over-sampling for imbalanced data making accuracy realistic and non-deceptive and can be Reliable.