AutismOnt: An Ontology-Driven Decision Support For Autism Diagnosis and Treatment

Hassan, Mariam M.; Mokhtar, Hoda M. O.

doi:10.1016/j.eij.2021.07.002

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…This is considered a gap that should be addressed and extensively covered. The authors of [59] dealt with the diagnosis and prediction of autism using ML algorithms based on medical and family characteristics. Therefore, facilitate access to ASD knowledge and support professionals and physicians in their clinical decisions by an ontologydriven decision support for autism diagnosis and treatment.…”

Section: Diagnosis Of Asd Based On Medical and Familymentioning

confidence: 99%

“…In addition, the customization of the ML allowed the merging of data from many varied sources, such as medical or intervention centers, hospitals, and academic centers, with help/support [5]. The authors' primary motivation in [59] recommended autism ontology, which is the major urge for developing algorithms based on subfield and is mainly used for calculating precision, pace, and customizability. Increased performance for the ML classifiers in diagnosing ASD was addressed using different algorithms regardless of available clinical tests [57].…”

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

“…That is why these children are not able to have proper treatment at an early age, which causes more complexity in their health [4]. Furthermore, this highly complex research environment makes it even harder for practicing physicians and primary care providers to keep up with recent advances [59].…”

Section: Misdiagnosis and Lack Of Awareness For Asdmentioning

confidence: 99%

“…(1) In the study [59], the authors recommended giving ideal decisions to doctors through good diagnosis and treatment, which in turn finds the best treatment method in addition to creating a road map by finding strengths, weaknesses, and services required based on factors, patient symptoms, and family medical history…”

Section: Recommendation and Futurementioning

confidence: 99%

See 3 more Smart Citations

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Alqaysi

Albahri

Hamid

2022

International Journal of Telemedicine and Applications

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Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” ( n = 39 ). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” ( n = 1 ). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

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Section: Diagnosis Of Asd Based On Medical and Familymentioning

confidence: 99%

Section: Improving Early Diagnosis and Treatmentmentioning

confidence: 99%

Section: Misdiagnosis and Lack Of Awareness For Asdmentioning

confidence: 99%

Section: Recommendation and Futurementioning

confidence: 99%

See 2 more Smart Citations

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Alqaysi

Albahri

Hamid

2022

International Journal of Telemedicine and Applications

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“…For classification, the linear discriminant analysis (LDA) and KNN algorithms are employed then evaluated by metrics such as CA, F1 score, and precision. The authors of [28], dealt with the diagnosis and prediction of autism using decision tree algorithm based on medical and family characteristics, therefore facilitating access to ASD knowledge and supporting professionals and physicians in their clinical decisions by An Ontology-Driven Decision Support for Autism Diagnosis and Treatment, and were evaluated by various metrics such as CA, specificity, and sensitivity. The data attributes are categorized under 13 categories: (1) diagnostic history, (2) review of systems, (3) prenatal/early postnatal history, (4) pulmonary, (5) developmental history, (6) hematologic, (7) endocrine/metabolic, (8) cardiovascular, (9) gastrointestinal, (10) current medications, (11) mental health, (12) genetic, and (13) immunologic.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

Alqaysi

Albahri

Hamid

2022

Computational and Mathematical Methods in Medicine

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Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose, prompting researchers to increase their efforts to find the best diagnosis by introducing machine learning (ML). Recently, several available challenges and issues have been highlighted for the diagnosis of ASD. High consideration must be taken into the feature selection (FS) approaches and classification process simultaneously by using medical tests and sociodemographic characteristic features in autism diagnostic. The constructed ML models neglected the importance of medical tests and sociodemographic features in a training and evaluation dataset, especially since some features have different contributions to the processing data and possess more relevancies to the classification information than others. However, the role of the physician’s experience towards feature contributions remains limited. In addition, the presence of many evaluation criteria, criteria trade-offs, and criteria importance categorize the evaluation and benchmarking of diagnosis ML models concerning the intersection between FS approaches and ML classification methods given under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for benchmarking the best hybrid diagnosis models to classify autism patients’ emergency levels considering multicriteria evaluation solutions. Method. The three-phase framework integrated the MCDM and ML to develop the diagnosis models and evaluate and benchmark the best. Firstly, the new ASD-dataset-combined medical tests and sociodemographic characteristic features is identified and preprocessed. Secondly, developing the hybrid diagnosis models using the intersection process between three FS techniques and five ML algorithms introduces 15 models. The selected medical tests and sociodemographic features from each FS technique are weighted before feeding the five ML algorithms using the fuzzy-weighted zero-inconsistency (FWZIC) method based on four psychiatry experts. Thirdly, (i) formulate a dynamic decision matrix for all developed models based on seven evaluation metrics, including classification accuracy, precision, F1 score, recall, test time, train time, and AUC. (ii) The fuzzy decision by opinion score method (FDOSM) is used to evaluate and benchmark the 15 models concerning the seven evaluation metrics. Results. Results reveal that (i) the three FS techniques have obtained a size different from the others in the number of the selected features; the sets were 39, 38, and 41 out of 48 features. Each set has its weights constructed by FWIZC. Considered sociodemographic features have been mostly selected more than medical tests within FS techniques. (ii) The first three best hybrid models were “ReF-decision tree,” “IG-decision tree,” and “Chi2-decision tree,” with score values 0.15714, 0.17539, and 0.29444. The best diagnosis model (ReF-decision tree) has obtained 0.4190, 0.0030, 0.9946, 0.9902, 0.9902, 0.9902, 0.9902, and 0.9951 for the C1=train time, C2=test time, C3=AUC, C4=CA, C5=F1 score, C6=precision, and C7=recall, respectively. The developed framework would be beneficial in advancing, accelerating, and selecting diagnosis tools in therapy with ASD. The selected model can identify severity as light, medium, or intense based on medical tests and sociodemographic weighted features.

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GDPR and FAIR Compliant Decision Support System Design for Triage and Disease Detection

Karamanlıoğlu

Sunar²,

Çetin³

et al. 2023

Advances in Intelligent Systems and Computing

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AutismOnt: An Ontology-Driven Decision Support For Autism Diagnosis and Treatment

Cited by 11 publications

References 15 publications

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review

Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework

GDPR and FAIR Compliant Decision Support System Design for Triage and Disease Detection

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