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

Hyde, Kayleigh; Novack, Marlena N.; LaHaye, Nicholas; Parlett-Pelleriti, Chelsea; Anden, Raymond; Dixon, Dennis R.; Linstead, Erik

doi:10.1007/s40489-019-00158-x

Cited by 158 publications

(77 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is a promising tool for investigating the replicability of patterns across larger, more heterogeneous datasets [11][12][13]. For automated diagnosis of ASD, personal characteristic (PC) data, such as intelligence quotient (IQ) and Social Responsiveness Scale (SRS) score have been adopted in several studies [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

show abstract

Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Although the prognosis of autism may be carried out at any age however the signs and symptoms generally appear inside the first 24 months of life and develop thru time [17]. ASD youngsters are identified for their impairment in social interplay skills and deciphering the emotional facial features in other [18]. Autism Spectrum Condition (ASC) is a fixed of neuro development syndromes that affects mind functions [21].…”

Section: Introductionmentioning

confidence: 99%

“…LOGISTIC REGRESSION (LR): LR is a statistical linear model used for binary class, taking as input a set of independent attributes. The independent attributes can be discrete or non-stop and are used to predict the possibility of the goal result taking a suitable binary value [18].  K-NN: K-NN is a kind of non-parametric or instance-based learning in which the undertaking is simplest anticipated domestically and all computation is deferred till classification.…”

mentioning

confidence: 99%

“…The boundary is defined as the distance between the unscrambling hyperplane and the training samples that are nearby to this hyperplane, which are the so-called support vectors [4].  NAIVE BAYES : Naive Bayes is a probabilistic classifier which makes use of Bayes theorem to make the assumption that each one enter attributes are linearly independent after which measure the possibility of every feasible classification given a set of autonomous attributes [23] [18].  RANDOM FOREST : A Random forest is a classifier such as a group of tree-structured classifiers {h(x, Ɵ k ) , k=1, …} where the { Ɵ k } are independent identically dispensed random vectors and each tree casts a unit vote for the maximum general class at input x [12].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

Priya*,

Radhika

2020

IJITEE

View full text Add to dashboard Cite

Autism Spectrum disorder (ASD) is a neurobiological developmental disorder is symbolize by means of the impairment of social interaction, stereotypic behaviours, and communiqué lack. Early deduction of ASD will enhance the fine of lifestyles of the affected person. The objective of the paper is to focus on the application of various Machine Learning strategies applied for the autism dataset for diagnosing ASD. In this study, the effective pre-processing techniques One-hot encoding, Splitting and Scaling are used to standardize the dataset and the Principal Component Analysis (PCA) evaluator method is applied for the best feature selection. This technique is investigated with various Machine learning techniques like Random Forest, SVM, Logistic Regression, KNN, Naive Bayes. Comparatively, the effective Pre-Processing technique with Random Forest model shows the better accuracy of 92% in diagnosing ASD. When with other metrics such as accuracy, precision, recall, F1-score, ROC, error rate.

show abstract

“…While review studies such as Hyde et al [24] and Thabtah [25] reported on ASD studies focusing on a single AI method, to our knowledge, no literature review has been conducted on the broad use of AI technology to distinguish individuals with ASD through an emphasis on behavioral aspects. Therefore, the aim of this study is to summarize findings on how AI can be implemented into the current evaluation process and explore other potential behavioral aspects that can be used to enhance efficiency in the detection of ASD.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

Song

Kim

Bong

et al. 2019

J Korean Acad Child Adolesc Psychiatry

View full text Add to dashboard Cite

Objectives:The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

show abstract

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

Cited by 158 publications

References 65 publications

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

Contact Info

Product

Resources

About