Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis

The Effectiveness of Android-Based Mobile Applications Authorized Early Detection on User Satisfaction (Parents, Health Empowerment, Teacher)

The 4.0 era education system requires the use of technology in pre-school and school activities. This study focuses on comparing the use of androin-based mobile applications for early detection of autism on user satisfaction (parents, health empowerment, teacher). This study was a quasi-experimental study with a post-test only control group design. A total of 30 parents, 30 health empowerment, and 30 educators in the city of Surakarta who were selected by purposive sampling were used as research samples. The samples were classified into two groups, namely by using an android-based mobile application for early detection of autism. User satisfaction data is collected through a user satisfaction questionnaire. Data were analyzed using univariate and bivariate with mann withney in the SPSS program. The results showed the influence of androin-based mobile applications early detection of autism on user satisfaction. The satisfaction of teaching staff users is higher than that of health empowerment and parents

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

A Review of Autism Spectrum Disorder Diagnostic Tools

This is an overview of diagnostic tools used in Malaysia. In this analysis, the benefits and disadvantages of each autism spectrum disorder diagnosis diagnostic technique are discussed. Pediatrists and psychiatric professionals who are focused on linguistic delay, brain function, or behavioural problems such as aggression, tantrum, etc. have currently noted the extent of impairments. The defects are assessed using multiple diagnostic tools. This review contrasts the seven forms of testing devices, including the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), Childhood Autism Rating Scale (CARS), Gillian Autism Rating Scale (GARS), Diagnostic Interview for Social and Communication Disorder (DISCO), Developmental, Dimensional & Diagnostic Interview (3DI), and Diagnostic and Statistical Manual of Mental Disorder (DSM). The advantages and limitations of each tool are discussed in detail

A Review of Autism Spectrum Disorder Diagnostic Tools

This is an overview of diagnostic tools used in Malaysia. In this analysis, the benefits and disadvantages of each autism spectrum disorder diagnosis diagnostic technique are discussed. Pediatrists and psychiatric professionals who are focused on linguistic delay, brain function, or behavioural problems such as aggression, tantrum, etc. have currently noted the extent of impairments. The defects are assessed using multiple diagnostic tools. This review contrasts the seven forms of testing devices, including the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), Childhood Autism Rating Scale (CARS), Gillian Autism Rating Scale (GARS), Diagnostic Interview for Social and Communication Disorder (DISCO), Developmental, Dimensional & Diagnostic Interview (3DI), and Diagnostic and Statistical Manual of Mental Disorder (DSM). The advantages and limitations of each tool are discussed in detail