Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD)

Abdullah, Azian Azamimi; Rijal, Saroja; Dash, Satya Ranjan

doi:10.1088/1742-6596/1372/1/012052

Cited by 30 publications

(13 citation statements)

References 2 publications

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“…The emphasis is on selecting the optimal features of autism and improving classification while maintaining high accuracy. Therefore, by reducing data dimensionality and choosing the appropriate and essential autism features, a machine learning algorithm will show promising results in diagnosing ASD [13,18,25,[52][53][54][55][56]. Nevertheless, several issues must be addressed, for instance, improper sampling methods, redundancy of features, imbalanced and insignificant data sizes, which influence accuracy.…”

Section: Discussionmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

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Section: Discussionmentioning

confidence: 99%

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

et al. 2020

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“…Work by Thabtah [11] and Abdullah et al [12], has agreed that ML using LR classifier perform good accuracy. Work by Thabtah [11] used information gain (IG) and tested with autism screening of adults and adolescents only.…”

Section: Feature Selection In Machine Learningmentioning

confidence: 93%

“…The same model should be tested for children dataset in future works and compare for better improvement. Where else, Abdullah et al [12] used the Least Absolute Shrinkage and Selection Operator (LASSO) as feature selection. The performance is improved as the number of features reduced from 19 to 13 features by using Chi-Square selected features, especially with Logistic Regression classifier present 100% accuracy.…”

Section: Feature Selection In Machine Learningmentioning

confidence: 99%

A Comparative Study on Autism Among Children Using Machine Learning Classification

Noruzman

Ghani

Zulkifli

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Autism Spectrum Disorder (ASD) is a neurodevelopment that affects communication and behavior in humans. It is a condition associated with a complex brain disorder, leading to significant changes in a human being's social interaction and behavior. Typically to detect toddlers who have ASD through screening tests is very expensive and time-consuming. Typically, detecting toddlers who have ASD through screening tests is very expensive and time-consuming. However, with machine learning technology today, autism can be diagnosed efficiency and accuracy. This study aims to analyze and make a comparison on which prediction model that gives a high accuracy after the feature selection. The importance of attributes is investigated using correlation and the predictive models are constructed for the detection of this disorder in children. The dataset consists of 1054 instances and each instance includes 19 attributes. Experimental results clearly show that using feature selection with 10 attributes can lead the impact of accuracy with predictive model of Random Forest (RF) returns the highest accuracy with 94.78%. The findings also indicated that the number of questions in screening tools can be reduced and give an impact with the good results.

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“…The outcome shows that the Ensemble Classification obtained the highest accuracy 100% compared to other models (Diabat & Al-shanableh, 2019). Abdullah, Rijal, & Dash (2019) worked on feature selection technique Chi-square and Least Absolute Shrinkage and Selection Operator (LASSO) and classification algorithm Random Forest, Logistic Regression and K-Nearest Neighbors. The outcome shows that the Logistic Regression scored the highest accuracy 97.541% with 13 selected features based on Chi-square FST (Abdullah, Rijal, & Dash, 2019).…”

Section: Related Workmentioning

confidence: 99%

Classification of Autistic Spectrum Disorder Using Deep Neural Network With Particle Swarm Optimization

Sahu¹,

Verma

2021

International Journal of Computer Vision and Image Processing

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In this paper, Feature Selection Technique (FST) namely Particle Swarm Optimization (PSO) has been used. The filter based PSO is a search method with Correlation-based Feature Selection (CBFS) as a fitness function. The FST has two key goals of improving classification efficiency and reducing feature counts. Artificial Neural Network (ANN) Based Multilayer Perceptron Network (MLP) and Deep Learning (DL) have been considered the classification methods on 2 benchmark Autistic Spectrum Disorder (ASD) dataset. The experimental result was compared to the non-reduced features and reduced feature of ASD datasets. The reduced feature give up enhanced results in both classifiers MLP and DL. In addition, an experimental study on the exhibitions of these methodologies has been conducted. Finally, a new trend of PSO-MLP and PSO-DL based classification model is proposed.

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Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD)

Cited by 30 publications

References 2 publications

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

A Comparative Study on Autism Among Children Using Machine Learning Classification

Classification of Autistic Spectrum Disorder Using Deep Neural Network With Particle Swarm Optimization

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