Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods

Alteneiji, Maitha Rashid; Mohammed, Layla; Usman, Muhammad

doi:10.14569/ijacsa.2020.0110929

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…Here, the linear support vector machine (SVM) method-based computerized method has been implemented for the diagnosis of ASD. Alteneiji et al (2020) looked into a number of ML methods to find an appropriate method for ASD prediction. Additionally, the goal of this work is to use algorithms trained with ML to diagnose autism more quickly so that therapy can be given to children at earlier and more significant developmental phases.…”

Section: Related Studiesmentioning

confidence: 99%

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Al-Muhanna,

Alghamdi,

Alrfaei

et al. 2024

Journal of Disability Research

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Autism spectrum disorder (ASD) is a neurological condition characterized by difficulties with communication and socializing, and repetitive activities. If the underlying reason is hereditary, early detection is still important, and machine learning offers a fascinating way to identify the condition more rapidly and economically. However, the unique issues of higher computational costs, longer execution times, and lower effectiveness affect the traditional methods. The proposed project aims to create an automated artificial intelligence tool for ASD identification that combines several state-of-the-art mining techniques to deliver the best possible level of disease prediction accuracy. For accurate and effective ASD identification, this research suggests an automated and lightweight method dubbed the auto-encoded warm equilibrium automated learner. To speed up the handicap detection process, a unique warm optimized feature selection methodology is applied to minimize the dimensionality of attributes. In addition, auto-encoded term memory equilibrium learning, a powerful deep learning technique, is designed to accurately and less frequently detect ASD from the given data. Moreover, the classifier performs better when hyperparameters are tuned using the equilibrium optimization model. The results of the proposed AE2L model have been tested and validated using a variety of parameters utilizing the well-known ASD dataset that was taken from the UCI repository.

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Section: Related Studiesmentioning

confidence: 99%

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Al-Muhanna,

Alghamdi,

Alrfaei

et al. 2024

Journal of Disability Research

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“…In this study, data preprocessing was utilized to improve the quality of data, to support us to build an accurate machine learning model [40]. The data preparation and preprocessing steps were implemented using machine learning methods.…”

Section: Data Cleaning and Preprocessingmentioning

confidence: 99%

Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms

Shearah,

Ullah,

Fakieh

2023

Diagnostics

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Children’s health is one of the most significant fields in medicine. Most diseases that result in children’s death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children’s urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child’s medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

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“…The aim is to examine dissimilar brain systems and define the functional connection for distinguishing among the subjects with ASD and assumed typically developing (TD). Alteneiji et al [15] focused on utilizing ML algorithm for predicting a person with ASD indications. The objective is to forecast a person having certain ASD symptom and finds an optimal ML method for detection.…”

Section: Introductionmentioning

confidence: 99%

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

Sree¹,

Kaur²,

Tikhonov³

et al. 2023

Computers, Materials &Amp; Continua

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Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification (JSODL-ASDDC) model. The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data. The proposed JSODL-ASDDC model initially performs min-max data normalization approach to scale the data into uniform range. In addition, the JSODL-ASDDC model involves JSO based feature selection (JFSO-FS) process to choose optimal feature subsets. Moreover, Gated Recurrent Unit (GRU) based classification model is utilized for the recognition and classification of ASD. Furthermore, the Bacterial Foraging Optimization (BFO) assisted parameter tuning process gets executed to enhance the efficacy of the GRU system. The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets. The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

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Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods

Cited by 12 publications

References 21 publications

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

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