FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309148720> ?p ?o ?g. }

• W4309148720 endingPage "19" @default.
• W4309148720 startingPage "1" @default.
• W4309148720 abstract "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." @default.
• W4309148720 created "2022-11-23" @default.
• W4309148720 creator A5004282253 @default.
• W4309148720 creator A5012505948 @default.
• W4309148720 creator A5017367751 @default.
• W4309148720 date "2022-11-16" @default.
• W4309148720 modified "2023-10-16" @default.
• W4309148720 title "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" @default.
• W4309148720 cites W158950929 @default.
• W4309148720 cites W2033401964 @default.
• W4309148720 cites W2218043766 @default.
• W4309148720 cites W2461102131 @default.
• W4309148720 cites W2477591619 @default.
• W4309148720 cites W2801805505 @default.
• W4309148720 cites W2884486887 @default.
• W4309148720 cites W2886793380 @default.
• W4309148720 cites W2891154333 @default.
• W4309148720 cites W2901443407 @default.
• W4309148720 cites W2920004509 @default.
• W4309148720 cites W2940599611 @default.
• W4309148720 cites W2946856752 @default.
• W4309148720 cites W2955693805 @default.
• W4309148720 cites W2964757079 @default.
• W4309148720 cites W2979111866 @default.
• W4309148720 cites W2987260336 @default.
• W4309148720 cites W2988828954 @default.
• W4309148720 cites W3010894680 @default.
• W4309148720 cites W3011016624 @default.
• W4309148720 cites W3011526340 @default.
• W4309148720 cites W3015625717 @default.
• W4309148720 cites W3016568881 @default.
• W4309148720 cites W3020375193 @default.
• W4309148720 cites W3025510279 @default.
• W4309148720 cites W3031443331 @default.
• W4309148720 cites W3047488904 @default.
• W4309148720 cites W3093225483 @default.
• W4309148720 cites W3093542640 @default.
• W4309148720 cites W3095750193 @default.
• W4309148720 cites W3096697136 @default.
• W4309148720 cites W3111137964 @default.
• W4309148720 cites W3121476122 @default.
• W4309148720 cites W3124627002 @default.
• W4309148720 cites W3127137920 @default.
• W4309148720 cites W3129829235 @default.
• W4309148720 cites W3134867620 @default.
• W4309148720 cites W3155346408 @default.
• W4309148720 cites W3158519178 @default.
• W4309148720 cites W3158650769 @default.
• W4309148720 cites W3159466359 @default.
• W4309148720 cites W3167491707 @default.
• W4309148720 cites W3173907537 @default.
• W4309148720 cites W3184824817 @default.
• W4309148720 cites W3190208880 @default.
• W4309148720 cites W3193347721 @default.
• W4309148720 cites W3194720052 @default.
• W4309148720 cites W3198021379 @default.
• W4309148720 cites W3202559501 @default.
• W4309148720 cites W4200085172 @default.
• W4309148720 cites W4205119394 @default.
• W4309148720 cites W4206430074 @default.
• W4309148720 cites W4206534028 @default.
• W4309148720 cites W4210542188 @default.
• W4309148720 cites W4212857100 @default.
• W4309148720 cites W4225482756 @default.
• W4309148720 cites W4229371401 @default.
• W4309148720 cites W427224453 @default.
• W4309148720 cites W4283783237 @default.
• W4309148720 cites W4288074363 @default.
• W4309148720 cites W4289172575 @default.
• W4309148720 cites W4295987324 @default.
• W4309148720 cites W4296946060 @default.
• W4309148720 cites W4297450601 @default.
• W4309148720 cites W4297831052 @default.
• W4309148720 cites W4300430800 @default.
• W4309148720 doi "https://doi.org/10.1155/2022/9410222" @default.
• W4309148720 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36439957" @default.
• W4309148720 hasPublicationYear "2022" @default.
• W4309148720 type Work @default.
• W4309148720 citedByCount "7" @default.
• W4309148720 countsByYear W43091487202023 @default.
• W4309148720 crossrefType "journal-article" @default.
• W4309148720 hasAuthorship W4309148720A5004282253 @default.
• W4309148720 hasAuthorship W4309148720A5012505948 @default.
• W4309148720 hasAuthorship W4309148720A5017367751 @default.
• W4309148720 hasBestOaLocation W43091487201 @default.
• W4309148720 hasConcept C11105738 @default.
• W4309148720 hasConcept C111919701 @default.
• W4309148720 hasConcept C118552586 @default.
• W4309148720 hasConcept C119857082 @default.
• W4309148720 hasConcept C124101348 @default.
• W4309148720 hasConcept C127413603 @default.
• W4309148720 hasConcept C13280743 @default.
• W4309148720 hasConcept C138885662 @default.
• W4309148720 hasConcept C142724271 @default.
• W4309148720 hasConcept C144133560 @default.
• W4309148720 hasConcept C146978453 @default.
• W4309148720 hasConcept C154945302 @default.
• W4309148720 hasConcept C162853370 @default.

• next