Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders

Akter, Tania; Satu, Md. Shahriare; Khan, Md. Imran; Ali, Mohammad Hanif; Uddin, Shahadat; Lió, Píetro; Quinn, Julian M.W.; Moni, Mohammad Ali

doi:10.1109/access.2019.2952609

Cited by 133 publications

(82 citation statements)

References 31 publications

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“…To further explore the cross-cultural validity of the instrument we applied the three ML classi ers to the 10 items selected by Allison and colleagues on the Q-CHAT-10 (2012) [19] and we found that, in our sample, SVM algorithm was able to classify autism with 87% accuracy, 65% sensitivity and 86% speci city. These results are in line with those recently reported by Akter et al (2019) [29], where a dataset of Q-CHAT-10 administered using a mobile application [27] was analyzed using ML and the SVM algorithm was able to classify autism with 98% of accuracy. Together, these ndings con rm a satisfactory cross-cultural validity of the Q-CHAT in different samples, countries and languages.…”

Section: Discussionsupporting

confidence: 91%

“…In the rst study, Thabath and colleagues (2019) [28], analyzed the adult version of the AQ-10 using a new rules-machine learning (RML) and were able to achieve an accuracy of about 90%, sensitivity of 87% and speci city of about 90%, while in the second study, the same authors applied the Naïve Bayes algorithm to the AQ-10 and found a similar accuracy of 92.8%, 91.3% and 95.7% for the child, adolescent and adult versions respectively. To the best of our knowledge, the only study applying ML to toddlers was conducted by Akter and colleagues (2019) [29], who analyzed the Q-CHAT-10 collected using the dataset from the ASDTests app and found that using a range of different classi ers, when optimized, they were able to effectively classify autism with an accuracy of 98%.…”

Section: Introductionmentioning

confidence: 99%

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Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

Tartarisco

Cicceri

Pietro

et al. 2020

Preprint

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Background: In the past two decades, several screening instruments have been developed to detect toddlers who may be autistic, both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q−CHAT) is a quantitative and normally distributed measure of autistic traits which demonstrated good psychometric properties in different settings and cultures. Recently machine learning (ML) has been applied to behavioural science to improve classification performance of autism screening and diagnostic tools, but mainly in children, adolescents and adults. Methods: In this study, we used machine learning (ML) to investigate the accuracy and reliability of the Q−CHAT in discriminating young autistic children from those without. Three different ML algorithms (Random Forest, Naive Bayes and Support Vector Machine) were applied to investigate the complete set of Q-CHAT items and the best predictive items. Results: Our results showed that the three selected models outperformed the classical statistical methods of predictive validity and among the three ML classifiers, the Support Vector Machine was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the Support Vector Machine-Recursive Feature Elimination approach we were able to select a subset of 14 items ensuring an accuracy of 93%, while an accuracy of 83% was obtained from the best 3 discriminating items in common to our and the previous reported Q-CHAT-10. Limitations: Further data collection is needed.Conclusions: This evidence confirms the high performance and cross-cultural validity of the Q-CHAT and supports the application of ML to create shorter and faster versions of the instrument maintaining high classification accuracy, to be used as a quick, easy and high-performance tool in primary care settings.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

Tartarisco

Cicceri

Pietro

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Previous studies have applied machine learning techniques to examine whether the process of diagnosing ASD can be improved by statistically identifying reduced subsets of features from existing diagnostic instruments reaching from self-administered screening questionnaires to clinician-administered diagnostic tools (for a recent overview, see Thabtah 32 ). A few authors have shown that efficiency and accessibility of existing pre-diagnostic screening questionnaires such as the Autism-Spectrum Quotient (AQ) [33][34][35] or the Social Responsiveness Scale (SRS) 36,37 can be improved using machine learning. Similar machine learning experiments have been run to identify subsets of behavioral features from clinician-administered diagnostic tools, namely ADOS (Module 1 to 3) [38][39][40][41][42] and ADI-R 36,39,43 .…”

mentioning

confidence: 99%

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

Küpper

Stroth

Wolff

et al. 2020

Sci Rep

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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. Autism Spectrum Disorders (ASD) comprise a range of pervasive neurodevelopmental disorders with a population prevalence of approximately 1% 1. They are characterized by early-onset persistent impairments in social communication and interaction as well as the presence of restricted, repetitive behaviors or interests 2,3. Diagnosing ASD is a complicated, lengthy and time-consuming process, which requires outstanding and specific clinical expertise 4,5. Although research makes constant progress in understanding the underlying genetic and neurobiological factors associated with ASD, there are currently no reliable biological markers for ASD and the diagnosis remains based on behavioral symptoms 1,6,7. The current so-called "gold standard" of ASD diagnosis comprises the use of various standardized diagnostic instruments that assist clinicians in reaching a best-estimate clinical diagnosis 7-9. Two of the most widely used diagnostic instruments are the Autism Diagnostic Observation Schedule (ADOS respectively ADOS-2 for the revised second edition) 10,11 and the Autism Diagnostic Interview

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“…In the voxel-based algorithms, Ecker et al performed ASD classification according to the local differences of voxel value in white matter and obtained 81% accuracy [17]. Uddin et al divided the original 3D MRI data into several small blocks, and analyzed the features of these small blocks.…”

Section: A Information Extraction Of Smrimentioning

confidence: 99%

Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

Yang

2020

IEEE Access

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Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders

Cited by 133 publications

References 31 publications

Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

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

Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

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