Machine Learning Classifiers for Autism Spectrum Disorder: A Review

Eman, Dadang; Emanuel, Andi Wahju Rahardjo

doi:10.1109/icitisee48480.2019.9003807

Cited by 17 publications

(5 citation statements)

References 24 publications

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“…With the use of machine learning on behalf of ASD, it should be able to speed up and increase the precision of their analytical decisions. By enhancing the precision and speed of conclusion to speed up the process of making judgments in the management of patients as soon as feasible so that patients receive faster treatment, ML can distinguish a condition, like diagnosing someone with a chemical imbalance [11].…”

Section: Related Workmentioning

confidence: 99%

Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

Saranya¹,

Anandan²

2022

Intelligent Automation &Amp; Soft Computing

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Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to only seven expressions, detection of ASD using facial emotions needs improvisation in terms of accurate detection and diagnosis. In this paper, we empirically relate the facial emotions to the ASD using the Facial Action Coding Systems (FACS) in which the different features are extracted by the FACS systems. For feature extraction, DEEPFACENET uses the FACS integrated Convolutional Neural Network (FACS-CNN) and hybrid Deep Learning of LSTM (Long Short-Term Memory) for the classification and detection of autism spectrum disorders (ASD). The experimentation is carried out using AFFECTNET databases and validated using Kaggle Autistic facial datasets (KAFD-2020). The Multi-Layer Perceptron (48.67%), Convolutional neural networks (67.75%), and Long ShortTerm Memory (71.56), the suggested model showed a considerable increase in recognition rate (92%), from this proposed model prove its superiority in detecting autistic facial emotions among children effectively.

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Section: Related Workmentioning

confidence: 99%

Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

Saranya¹,

Anandan²

2022

Intelligent Automation &Amp; Soft Computing

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“…A traditional artificial intelligence (AI)-based CADS encompasses several stages of data acquisition, data preprocessing, feature extraction, and classification [29], [30], [31], [32]. In [33], [34], [35] existing traditional algorithms for diagnosing ASD have been reviewed. In contrast to traditional methods, in DL-based CADS, feature extraction, and classification are performed intelligently within the model.…”

Section: Cads-based Deep Learning Techniques For Asd Diagnosis By Neu...mentioning

confidence: 99%

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Khodatars,

Shoeibi,

Sadeghi

et al. 2020

Preprint

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Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation. Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated. Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed. Finally, we presented important challenges in the automated detection and rehabilitation of ASD.

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“…The recommended algorithms are SVM, random forest, decision trees and logistic regression. Conversely, in Reference 76. ASD analysis was conducted using supervised ML algorithms (e.g., decision tree, random forest, SVM, Naive Bayes, generalized linear model, logistic regression and K‐nearest neighbor [kNN]), and the most widely used algorithms in the literature are SVM, random forest and decision tree.…”

Section: Introductionmentioning

confidence: 99%

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

Albahri

Zaidan

Alsattar³

et al. 2022

Computational Intelligence

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Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively.

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Machine Learning Classifiers for Autism Spectrum Disorder: A Review

Cited by 17 publications

References 24 publications

Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T‐spherical fuzzy‐weighted zero‐inconsistency method

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