[Retracted] Prediction and Analysis of Autism Spectrum Disorder Using Machine Learning Techniques

Abstract

Autism spectrum disorder is a severe, life-prolonged neurodevelopmental disease typified by disabilities that are chronic or limited in the development of socio-communication skills, thinking abilities, activities, and behavior. In children aged two to three years, the symptoms of autism are more evident and easier to recognize. The major part of the existing literature on autism spectrum disorder is covered by a prediction system based on traditional machine learning algorithms such as support vector machine, random forest, multiple layer perceptron, naive Bayes, convolution neural network, and deep neural network. The proposed models are validated by using performance measurement parameters such as accuracy, precision, and recall. In this research, autism spectrum disorder prediction has been investigated and compared using common parameters such as application type, simulation method, comparison methodology, and input data. The key purpose of this study is to give a centralized framework to use for researchers working on autism spectrum disorder prediction. The best results were obtained by using the random forest algorithm as it performs better than other traditional machine learning algorithms. The achieved accuracy is 89.23%. The workflow representations of the investigated frameworks assist readers in comprehending the fundamental workings and architectures of these frameworks.

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

“…The MLP technique is utilized for its capacity to solve nonlinear problems and for handling huge datasets. Several studies have utilized MLP (Rahman et al, 2020;Umamaheswari et al, 2021;Rahman and Mamun, 2022;Qureshi et al, 2023) for the identification process due to its advantages in several aspects like nonlinearity, flexibility, parallel processing, robustness, and universal approximator.…”

Towards an Autism Spectrum Disorder (ASD) care agent for social interaction

“…In response to the same, the shopkeeper asks for the doctor's prescription (line 4). Here, the assumption is that the subject asks for the medicines that cannot be purchased without a doctor's prescription 10 . The shopkeeper then hands over the medicine to the subject (line 5), and the subject asks for the bill amount (line 6).…”

“…Early detection of ASD is better because as the age of a person increases, the detection of ASD becomes difficult due to the onset of overlap of it's symptoms with those of other diseases. Lately, ASD has gained a lot of attention and interest as a research field, which is evident from numerous recent publications [6][7][8][9][10][11][12]. In majority of these works, machine learning (ML)/ deep learning (DL) models are trained on the (publicly) available datasets and then their performances are tested using the instances from the publicly available datasets or through a set of real-life data values.…”

“…We feel that the use of artificial intelligence (AI) for design of patient centric platforms is quite crucial for achieving this SDG. In all the published works [6][7][8][9][10][11][12], the focus has been 1 Cognitive-based neurological disorders encompass a wide spectrum of conditions affecting the cognitive function(s) in children, with diverse causes, symptoms, and impacts. These disorders are classified into different categories based on their underlying pathology.…”

Prediction of Autism Spectrum Disorder in Children: A Detailed Systematic Review on Machine Learning and Deep Learning Methods

“…Systems that use ML learn from experience without having that knowledge explicitly built into them. In order to accurately classify and predict various sorts of data, ML is utilized [3]. DL is a branch of machine learning that produces superior outcomes compared to ML techniques.…”