Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervasive developmental disorders. The accuracy and efficiency of these screening methods rely primarily on the experience and knowledge of the user, as well as the items designed in the screening method. One promising direction to improve the accuracy and efficiency of autism spectrum disorder detection is to build classification systems using intelligent technologies such as machine learning. Machine learning offers advanced techniques that construct automated classifiers that can be exploited by users and clinicians to significantly improve sensitivity, specificity, accuracy, and efficiency in diagnostic discovery. This article proposes a new machine learning method called Rules-Machine Learning that not only detects autistic traits of cases and controls but also offers users knowledge bases (rules) that can be utilized by domain experts in understanding the reasons behind the classification. Empirical results on three data sets related to children, adolescents, and adults show that Rules-Machine Learning offers classifiers with higher predictive accuracy, sensitivity, harmonic mean, and specificity than those of other machine learning approaches such as Boosting, Bagging, decision trees, and rule induction.

Autistic spectrum disorder (ASD) refers to a neurodevelopmental condition associated with verbal and nonverbal communication, social interactions, and behavioural complications that is becoming increasingly common in many parts of the globe. Identifying individuals on the spectrum has remained a lengthy process for the past few decades due to the fact that some individuals diagnosed with ASD exhibit exceptional skills in areas such as mathematics, arts, and music among others. To improve the accuracy and reliability of autism diagnoses, many scholars have developed pre-diagnosis screening methods to help identify autistic behaviours at an early stage, speed up the clinical diagnosis referral process, and improve the understanding of ASD for the different stakeholders involved, such as parents, caregivers, teachers, and family members. However, the functionality and reliability of those screening tools vary according to different research studies and some have remained questionable. This study evaluates and critically analyses 37 different ASD screening tools in order to identify possible areas that need to be addressed through further development and innovation. More importantly, different criteria associated with existing screening tools, such as accessibility, the fulfilment of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) specifications, comprehensibility among the target audience, performance (specifically sensitivity, specificity, and accuracy), web and mobile availability, and popularity have been investigated.

We report an investigation into the processes involved in a common graph-reading task using two types of Cartesian graph. We describe an experiment and eye movement study, the results of which show that optimal scan paths assumed in the task analysis approximate the detailed sequences of saccades made by individuals. The research demonstrates the computational inequivalence of two sets of informationally equivalent graphs and illustrates how the computational advantages of a representation outweigh factors such as user unfamiliarity. We describe two models, using the ACT rational perceptual motor (ACT-R/PM) cognitive architecture, that replicate the pattern of observed response latencies and the complex scan paths revealed by the eye movement study. Finally, we outline three guidelines for designers of visual displays: Designers should (a) consider how different quantities are encoded within any chosen representational format, (b) consider the full range of alternative varieties of a given task, and (c) balance the cost of familiarization with the computational advantages of less familiar representations. Actual or potential applications of this research include informing the design and selection of appropriate visual displays and illustrating the practice and utility of task analysis, eye tracking, and cognitive modeling for understanding interactive tasks with external representations.