PMID- 31154550
OWN - NLM
STAT- MEDLINE
DCOM- 20200102
LR  - 20200225
IS  - 1573-689X (Electronic)
IS  - 0148-5598 (Linking)
VI  - 43
IP  - 7
DP  - 2019 Jun 1
TI  - Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and 
      Classification Based on BWM and Group-VIKOR.
PG  - 212
LID - 10.1007/s10916-019-1338-x [doi]
AB  - This paper aims to assist the administration departments of medical organisations 
      in making the right decision on selecting a suitable multiclass classification 
      model for acute leukaemia. In this paper, we proposed a framework that will aid 
      these departments in evaluating, benchmarking and ranking available multiclass 
      classification models for the selection of the best one. Medical organisations 
      have continuously faced evaluation and benchmarking challenges in such endeavour, 
      especially when no single model is superior. Moreover, the improper selection of 
      multiclass classification for acute leukaemia model may be costly for medical 
      organisations. For example, when a patient dies, one such organisation will be 
      legally or financially sued for incidents in which the model fails to fulfil its 
      desired outcome. With regard to evaluation and benchmarking, multiclass 
      classification models are challenging processes due to multiple evaluation and 
      conflicting criteria. This study structured a decision matrix (DM) based on the 
      crossover of 2 groups of multi-evaluation criteria and 22 multiclass 
      classification models. The matrix was then evaluated with datasets comprising 72 
      samples of acute leukaemia, which include 5327 gens. Subsequently, multi-criteria 
      decision-making (MCDM) techniques are used in the benchmarking and ranking of 
      multiclass classification models. The MCDM used techniques that include the 
      integrated BWM and VIKOR. BWM has been applied for the weight calculations of 
      evaluation criteria, whereas VIKOR has been used to benchmark and rank 
      classification models. VIKOR has also been employed in two decision-making 
      contexts: individual and group decision making and internal and external group 
      aggregation. Results showed the following: (1) the integration of BWM and VIKOR 
      is effective at solving the benchmarking/selection problems of multiclass 
      classification models. (2) The ranks of classification models obtained from 
      internal and external VIKOR group decision making were almost the same, and the 
      best multiclass classification model based on the two was 'Bayes. Naive Byes 
      Updateable' and the worst one was 'Trees.LMT'. (3) Among the scores of groups in 
      the objective validation, significant differences were identified, which 
      indicated that the ranking results of internal and external VIKOR group decision 
      making were valid.
FAU - Alsalem, M A
AU  - Alsalem MA
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia.
AD  - Department of Management Information System, College of Administration and 
      Economic, University of Mosul, Mosul, Iraq.
FAU - Zaidan, A A
AU  - Zaidan AA
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia. aws.alaa@gmail.com.
FAU - Zaidan, B B
AU  - Zaidan BB
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia.
FAU - Albahri, O S
AU  - Albahri OS
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia.
FAU - Alamoodi, A H
AU  - Alamoodi AH
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia.
FAU - Albahri, A S
AU  - Albahri AS
AD  - College of Engineering, University of Information Technology and Communications, 
      Baghdad, Iraq.
FAU - Mohsin, A H
AU  - Mohsin AH
AD  - Republic of Iraq-Presidency of Ministries - Establishment of Martyrs, Baghdad, 
      Iraq.
FAU - Mohammed, K I
AU  - Mohammed KI
AD  - Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, 
      Perak, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20190601
PL  - United States
TA  - J Med Syst
JT  - Journal of medical systems
JID - 7806056
SB  - IM
MH  - Bayes Theorem
MH  - *Decision Support Techniques
MH  - Humans
MH  - Leukemia, Myeloid, Acute/*diagnosis/*pathology
MH  - Sensitivity and Specificity
MH  - Time Factors
OTO - NOTNLM
OT  - Acute leukaemia
OT  - BWM
OT  - Benchmarking
OT  - Classification
OT  - Multiclass evaluation
OT  - VIKOR
EDAT- 2019/06/04 06:00
MHDA- 2020/01/03 06:00
CRDT- 2019/06/03 06:00
PHST- 2019/03/04 00:00 [received]
PHST- 2019/05/13 00:00 [accepted]
PHST- 2019/06/03 06:00 [entrez]
PHST- 2019/06/04 06:00 [pubmed]
PHST- 2020/01/03 06:00 [medline]
AID - 10.1007/s10916-019-1338-x [pii]
AID - 10.1007/s10916-019-1338-x [doi]
PST - epublish
SO  - J Med Syst. 2019 Jun 1;43(7):212. doi: 10.1007/s10916-019-1338-x.