ASD classification for children using deep neural network

Mohanty, Ashima Sindhu; Parida, Priyadarsan; Patra, Krishna Chandra

doi:10.1016/j.gltp.2021.08.042

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…It is evident from the results in Table 6 that the feature signatures generated by AutoML models, aid in improving classifcation accuracy. Te authors aim to extend this work to biosignature detection for ASD and computationally [24] 99 C4.5 [24] 96 Deep neural network [23] 92 SGD (stochastic gradient descent) [7] 99.6 Random forest [9] 97.2 Soft voting classifer [8] 94. identify genetic variants/mutants that could aid in autism therapy and study the efciency of AutoML on a multiclass categorization of neuro-developmental disorders. Tis work will also be expanded to evaluate the role of feature construction methods on genetic/image data for autism classifcation and assess the performance of AutoML models on autism classifcation with the new features generated.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies were undertaken on classifying ASD from images through traditional machine learning models, hybrid models that infuse feature selection with classifcation, deep learning models, and AutoML models [20,[23][24][25][26][27]. A concise review of the recent research on ASD classifcation is depicted in Table 1.…”

Section: Literature Surveymentioning

confidence: 99%

“…Mohanty et al [ 23 ] reported their classification results on publicly accessible and authenticated ASD datasets from the UCI machine learning (ML) repository and Kaggle. The authors proposed a deep learning-based classifier on the child data set gathered from the UCI repository.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework

Jacob¹,

Sulaiman²,

Bennet³

2023

Computational Intelligence and Neuroscience

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Autism spectrum disorder is the most used umbrella term for a myriad of neuro-degenerative/developmental conditions typified by inappropriate social behavior, lack of communication/comprehension skills, and restricted mental and emotional maturity. The intriguing factor of this disorder is attributed to the fact that it can be detected only by close monitoring of developmental milestones after childbirth. Moreover, the exact causes for the occurrence of this neurodevelopmental condition are still unknown. Besides, autism is prevalent across individuals irrespective of ethnicity, genetic/familial history, and economic/educational background. Although research suggests that autism is genetic in nature and early detection of this disorder can greatly enhance the independent lifestyle and societal adaptability of affected individuals, there is still a great dearth of information to support the statement of proven facts and figures. This research work places emphasis on the application of automated machine learning incorporated with feature ranking techniques to generate significant feature signatures for the early detection of autism. Publicly available datasets based on the Q-chat scores of individuals across diverse age groups—toddlers, children, adolescents, and adults have been employed in this study. A machine learning framework based on automated hyperparameter optimization is proposed in this work to rank the potential nonclinical markers for autism. Moreover, this study aimed at ranking the AutoML models based on Mathew’s correlation coefficient and balanced accuracy via which nonclinical markers were identified from these datasets. Besides, the feature signatures and their significance in distinguishing between classes are being reported for the first time in autism detection. The proposed framework yielded ∼90% MCC and ∼95% balanced accuracy across all four age groups of autism datasets. Deep learning approaches have yielded a maximum of 92.7% accuracy on the same datasets but are limited in their ability to extract significant markers, have not reported on MCC for unbalanced data, and cannot adapt automatically to new data entries. However, AutoML approaches are more flexible, easier to implement, and provide automated optimization, thereby yielding the highest accuracy with minimal user intervention.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework

Jacob¹,

Sulaiman²,

Bennet³

2023

Computational Intelligence and Neuroscience

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“…ML methods give good predictions for ASD and can be used in the early stages for identification and diagnosis. In order to investigate the underlying structural and strategic foundations of ASD, researchers have employed deep learning techniques [18], utilizing 14 distinct models including convolutional neural network (CNN) and recurrent neural network (RNN) to analyze 1,000 magnetic resonance imaging (MRI) scan images [19]. This approach enables the identification of specific brain structures that are indicative of a complex psychiatric condition, while also streamlining and optimizing the diagnostic process for clinicians in terms of time and resources.…”

Section: Introductionmentioning

confidence: 99%

A computational intelligent analysis of autism spectrum disorder using machine learning techniques

Mareeswaran,

Selvarajan

2024

IJ-AI

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<p>Children between the ages of 12 and 24 months who have autism spectrum disorder (ASD) experience abnormalities in the brain that result in undesirable symptoms. Children with ASD struggle to comprehend what others are trying to say and or feel, and they experience extreme anxiety in social situations. Additionally, they have a hard time making friends and even living independently. The defective genes, which control the brain and govern how brain cells communicate with one another, are the primary cause of ASD because they alter brain function. Our primary goal is to assist therapists and parents of children with ASD in using current technologies, such as human intelligence and artificial intelligence, to treat ASD and assist those youngsters in obtaining better social interaction and societal integration. For the purpose of doing an early analysis of ASD, the data is divided into the following three categories: age, gender, and jaundice symptoms. The performance of machine learning algorithms can be influenced by a variety of factors, such as the size of the dataset and quality of the dataset, the choice of features, and the tuning of hyper-parameters. In this work, the support vector machine (SVM) yields 96% as the highest classification accuracy.</p>

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“…Some research papers have used machine learning techniques to analyze EEG data, achieving high accuracy in distinguishing between individuals with autism and those without it [17][18][19][20][21][22][23]. Others have suggested deep learning methods that extract key features from EEG signals, showing promising results in autism detection [24][25][26][27]. Schwartz et al focused on investigating speci c EEG frequency bands and their correlation with autism, revealing distinct patterns that could aid in identi cation [28,29].…”

Section: Introductionmentioning

confidence: 99%

Autistic recognition from EEG signals by extracted features from several time series models

Rajabioun

2024

Preprint

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Autism is a neurological and psychological disorder that typically manifests in childhood and persists into adulthood. It is characterized by atypical social, behavioral, and communication skills, as well as diminished attention to the surrounding environment. The detection and recognition of autism can contribute to the prevention of its development and the enhancement of social and communicational abilities. Various methods are employed for autism recognition, including questionnaire tests and neurological techniques. One such neuroimaging method is electroencephalography (EEG), which records the brain's electrical activities through sensors placed on the scalp. This paper proposes a method for identifying individuals with autism using EEG signals and features extracted from a multivariate autoregressive moving average (MVARMA) and multivariate integrated autoregressive(ARIMA) models. The approach begins by estimating active sources through source localization methods, followed by the application of a dual Kalman filter to estimate source activity. Subsequently, the MVARMA and ARIMA models are applied to the EEG sensor and active source data, enabling the calculation of model parameters. Principal component analysis is then utilized to select important parameters, and a K nearest neighbor classifier is employed to classify participants as either autistic or neurotypical. The results demonstrate superior classification performance, achieving higher accuracy compared to alternative methods. The proposed method yields superior classification outcomes when compared to other approaches, as it exhibits improved classification measures.

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ASD classification for children using deep neural network

Cited by 10 publications

References 24 publications

Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework

Feature Signature Discovery for Autism Detection: An Automated Machine Learning Based Feature Ranking Framework

A computational intelligent analysis of autism spectrum disorder using machine learning techniques

Autistic recognition from EEG signals by extracted features from several time series models

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