Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Farooq, Muhammad Shoaib; Tehseen, Rabia; Sabir, Maidah; Atal, Zabihullah

doi:10.1038/s41598-023-35910-1

Cited by 27 publications

(8 citation statements)

References 59 publications

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“…Furthermore, other ensemble models can be employed to combine the predictions of multiple models with different architectures, hyperparameters, or initializations, using techniques such as averaging or stacking to reduce model biases and errors and enhance the model's generalization capabilities. (Biswas et al, 2021) 98.27 98.27 Deep learning (Garg et al, 2022) 98 98 Federated learning (Farooq et al, 2023) 98 98…”

Section: Discussionmentioning

confidence: 99%

EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning

Atlam,

Masud,

Rokaya

et al. 2024

Journal of Disability Research

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A neuro-developmental disorder known as autism spectrum disorder (ASD) affects a significant portion of the global population. Those with ASD frequently struggle to interact and communicate with others and may engage in restricted or repetitive behaviors or interests. The symptoms of autism begin early in childhood and can continue into adulthood. Machine learning and deep learning (DL) models are employed in clinical research for the early identification and diagnosis of ASD. However, the majority of the existing models lack interpretability in their results for ASD diagnosis. The explainable artificial intelligence (XAI) concepts can be used to provide transparent and understandable explanations for models’ decisions. In this work, we present an explainable autism spectrum disorder model based on DL for autism disorder detection in toddlers and children. The primary objective of this study is to better understand and interpret the classification process and to discern the significant features that contribute to the prediction of ASD. The proposed model is divided into two distinct components. The first component employs a DL model for autism disorder detection. The second uses an XAI technique known as shapley additive explanations (SHAP) to emphasis key characteristics and explain the model’s outcomes. The model showed perfect performance on the training set, with an accuracy of 1 and a receiver operating characteristic score of 1. On the test set, the model achieved an accuracy score of 0.9886, indicating that it performed nearly as well as on the training set. The experimental results demonstrate that the proposed model has the capability to accurately predict and diagnose ASD while also providing explanatory insights into the obtained results. Furthermore, the results indicate that the proposed model performs competitively compared to the state-of-the-art models in terms of accuracy and F1-score. The results highlight the efficacy and potential of the proposed model in accurately predicting ASD in binary classification tasks.

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Section: Discussionmentioning

confidence: 99%

EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning

Atlam,

Masud,

Rokaya

et al. 2024

Journal of Disability Research

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“…Despite PART, SVM, and ANN performing better, the proposed system outperformed them with a 2% increase in accuracy. (Farooq et al, 2023). Abbreviations: LR, logistic regression; SVM, support vector machine.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Figure 14:Comparative examination of the propounded model with the traditional system(Farooq et al, 2023). Abbreviations: LR, logistic regression; SVM, support vector machine.…”

mentioning

confidence: 99%

Trailblazing ASD Behavioral Research: Leveraging Modified MLP with Cross-Weighted Attention Mechanism at the Forefront

Sha,

Alqahtani,

Alsubai

et al. 2024

Journal of Disability Research

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Autism spectrum disorder (ASD) is an enduring and intricate condition that encompasses issues with behavior and communication. This disability has become common among several individuals globally. To ensure that all the autistic individuals get the correct support and care throughout their lifetime, it is significant that the autism signs are realized and the condition is spotted in the initial phase itself to avoid further complications. However, the process of ASD diagnosis is time-consuming, with expensive testing procedures. An effective screening method is needed for early treatment to improve the quality of the ASD patient’s life. Several conventional studies thrived on attaining ASD identification with machine learning and deep learning) but lacked in accuracy and computation. Therefore, the propounded system employs modified multi-layer perceptron (MLP) with cross-weighted attention mechanism. MLP is utilized for its capability in resolving intricate nonlinear complications, handling huge datasets, and enhancing the accuracy of the model. Though existing studies have utilized MLP for various identification processes, it still lacks identification speed, has overfitting issues, and requires more parameters. To overcome these issues, the proposed system employs cross-weighted attention mechanism, to enhance the identification process. As few researchers focused only on identifying and diagnosing the ASD adult, the proposed system uses autism screening in adult datasets and intends to improve the diagnosis of ASD adult patients. Attention ASD-modified MLP with cross-weighted algorithm is applied to classify and perform with various algorithms such as random forest, MLP, and Naïve Bayes. Furthermore, the performance is examined with certain metrics to calculate the efficacy of the proposed system.

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“…After addressing the missing data and applying machine learning models, the results highlight the superiority of Convolutional Neural Network (CNN)-based prediction models, achieving remarkably high accuracy rates of 98.30%, 96.88%, and 99.53% for ASD screening in children, adolescent, and adult populations, respectively. In [8], federal learning is applied to achieve 98% and 81% accuracy for ASD child and adult datasets, respectively. A detailed review about ASD is presented in [9,10].…”

Section: Literature Reviewmentioning

confidence: 99%