The diagnosis of ASD using multiple machine learning techniques

Kumar, Chitranjan; Das, Priti

doi:10.1080/20473869.2021.1933730

Cited by 26 publications

(15 citation statements)

References 26 publications

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“…With respect to the latter, a convolutional neural networks (CNN) model was used, which achieved a 0.82 test accuracy, higher than the best accuracy we obtained with our federated learning model equal to 0.6 with 3 clients being available. As for the work done in [40] for regular ML on ASD behavioral screening data, their accuracy noted was 0.9 with the artificial neural networks (ANN) model, which is also higher than the accuracy achieved in our experiments equals to 0.7 per 3 clients in the experiment.…”

Section: Discussioncontrasting

confidence: 62%

“…In comparison to regular ML models, higher accuracy is achieved in [40] for ASD screening behavioral data and in Kaggle 2 for ASD facial image data. With respect to the latter, a convolutional neural networks (CNN) model was used, which achieved a 0.82 test accuracy, higher than the best accuracy we obtained with our federated learning model equal to 0.6 with 3 clients being available.…”

Section: Discussionmentioning

confidence: 99%

“…According to [40], who used the same behavioral dataset we aim at using in this paper, using random forests, decision trees, and support vector machine algorithms result in around 90% accuracy, which improves with using feature selection methods. A remarkable dataset used in the literature was ABIDE dataset consisted of MRI neuro-images in [41] and [42].…”

Section: Related Workmentioning

confidence: 99%

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Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

Shamseddine¹

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Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain pre-birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior, in addition to specific behavioral traits. Hence, this would possibly deteriorate their social behavior among other individuals, as well as their overall interaction within their community. Moreover, medical research has proved that ASD also affects the facial characteristics of its patients, making the syndrome recognizable from distinctive signs within an individual’s face. Given that as a motivation behind our work, we propose a novel privacy-preserving Federated Learning scheme to predict ASD in a certain individual based on their behavioral and facial features, embedding a merging process of both data features through facial feature extraction, while respecting patient data privacy. After training behavioral and facial image data on Federated Machine Learning models, promising results are achieved, with 70% accuracy for prediction of ASD according to behavioral traits in a federated learning private environment, and a 62% accuracy is reached for prediction of ASD given an image of the patient’s face. Then, we test the behavior of regular as well as federated learning on our merged data, behavioral and facial, where a 65% accuracy is achieved with regular logistic regression model and 63% accuracy with federated learning model.

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Section: Discussioncontrasting

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

Shamseddine¹

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“…Fifteen studies have been detected [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] in this part of the overview. In some articles, the intersection of AI and ATs is directly faced [33][34][35][36][37][38][39][40][41][42][43][45][46][47], while in others, it is faced more with an in-perspective overview [44].…”

Section: Analysis In Detailsmentioning

confidence: 99%

“…Kumar et al [36] examine the automation of ASD diagnosis using machine learning techniques. The study leverages AI to analyze a dataset of 701 samples, aiming to develop models that can assist in diagnosing ASD automatically.…”

Section: Analysis In Detailsmentioning

confidence: 99%

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Iannone,

Giansanti

2023

JPM

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(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.

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Automatic Schizophrenia Detection Using Discrete Wavelet Transform from EEG Signal

Bhadra,

Kumar

2024

Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology

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The diagnosis of ASD using multiple machine learning techniques

Cited by 26 publications

References 26 publications

Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

Federated Machine Learning for Multi-Aspect Neuro-developmental Disorders

Breaking Barriers—The Intersection of AI and Assistive Technology in Autism Care: A Narrative Review

Automatic Schizophrenia Detection Using Discrete Wavelet Transform from EEG Signal

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