PMID- 36439957
OWN - NLM
STAT- MEDLINE
DCOM- 20221129
LR  - 20221129
IS  - 1748-6718 (Electronic)
IS  - 1748-670X (Print)
IS  - 1748-670X (Linking)
VI  - 2022
DP  - 2022
TI  - 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.
PG  - 9410222
LID - 10.1155/2022/9410222 [doi]
LID - 9410222
AB  - 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 "Chi(2)-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.
CI  - Copyright (c) 2022 M. E. Alqaysi et al.
FAU - Alqaysi, M E
AU  - Alqaysi ME
AD  - Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for 
      Computers and Informatics (ICCI), Baghdad, Iraq.
AD  - Department of Medical Instruments Engineering Techniques, Al-Farahidi University, 
      Baghdad 10021, Iraq.
FAU - Albahri, A S
AU  - Albahri AS
AUID- ORCID: 0000-0003-3335-457X
AD  - Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for 
      Computers and Informatics (ICCI), Baghdad, Iraq.
FAU - Hamid, Rula A
AU  - Hamid RA
AD  - College of Business Informatics, University of Information Technology and 
      Communications (UOITC), Baghdad, Iraq.
LA  - eng
PT  - Journal Article
DEP - 20221116
PL  - United States
TA  - Comput Math Methods Med
JT  - Computational and mathematical methods in medicine
JID - 101277751
SB  - IM
MH  - Humans
MH  - *Benchmarking
MH  - *Autistic Disorder/diagnosis
MH  - Machine Learning
MH  - Algorithms
PMC - PMC9683965
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2022/11/29 06:00
MHDA- 2022/11/30 06:00
PMCR- 2022/11/16
CRDT- 2022/11/28 04:47
PHST- 2022/08/19 00:00 [received]
PHST- 2022/10/01 00:00 [revised]
PHST- 2022/10/18 00:00 [accepted]
PHST- 2022/11/28 04:47 [entrez]
PHST- 2022/11/29 06:00 [pubmed]
PHST- 2022/11/30 06:00 [medline]
PHST- 2022/11/16 00:00 [pmc-release]
AID - 10.1155/2022/9410222 [doi]
PST - epublish
SO  - Comput Math Methods Med. 2022 Nov 16;2022:9410222. doi: 10.1155/2022/9410222. 
      eCollection 2022.