PMID- 37673562
OWN - NLM
STAT- MEDLINE
DCOM- 20230908
LR  - 20231213
IS  - 1873-2860 (Electronic)
IS  - 0933-3657 (Linking)
VI  - 143
DP  - 2023 Sep
TI  - Monkeypox diagnosis using ensemble classification.
PG  - 102618
LID - S0933-3657(23)00132-X [pii]
LID - 10.1016/j.artmed.2023.102618 [doi]
AB  - The world has recently been exposed to a fierce attack from many viral diseases, 
      such as Covid-19, that exhausted medical systems around the world. Such attack 
      had a negative impact not only on the health status of people or the high death 
      rate, but also had a bad impact on the economic situation, which affected all 
      countries of the world especially the poor and the developing ones. Monkeypox is 
      one of the latest viral diseases that may cause a pandemic in the near future if 
      not dealt and diagnosed with appropriately. This paper provides a new strategy 
      for diagnosing monkeypox, which is called; Accurate Monkeypox Diagnosing Strategy 
      (AMDS). The proposed AMDS consists of two phases, which are; (i) pre-processing 
      and (ii) classification. During the pre-processing phase, the most effective 
      feature are selected using Binary Tiki-Taka Algorithm (BTTA). On the other hand, 
      in the classification phase, ensemble classification is used for diagnosing new 
      cases, which combines evidence from three different new classifiers, namely; (a) 
      Layered K-Nearest Neighbors (LKNN), (b) Statistical Naive Bayes (SNB), and (c) 
      Deep Learning Classifier (DLC). Moreover, the decisions of the proposed 
      classifiers are merged in a new voting scheme called Fuzzified Voting Scheme 
      (FVS). AMDS has been compared against recent diagnostic strategies. Experimental 
      results have proven that AMDS outperforms other monkeypox diagnostic strategies 
      as it introduces the most accurate diagnosis according to two different datasets.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Rabie, Asmaa H
AU  - Rabie AH
AD  - Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura 
      University, Mansoura, Egypt.
FAU - Saleh, Ahmed I
AU  - Saleh AI
AD  - Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura 
      University, Mansoura, Egypt. Electronic address: aisaleh@yahoo.com.
LA  - eng
PT  - Journal Article
DEP - 20230701
PL  - Netherlands
TA  - Artif Intell Med
JT  - Artificial intelligence in medicine
JID - 8915031
SB  - IM
MH  - Humans
MH  - *Mpox (monkeypox)/diagnosis
MH  - Bayes Theorem
MH  - *COVID-19/diagnosis
MH  - Algorithms
MH  - Cluster Analysis
MH  - COVID-19 Testing
OTO - NOTNLM
OT  - Classification
OT  - Diagnosis
OT  - Feature selection
OT  - Monkeypox
OT  - Tiki-Taka Algorithm
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/09/07 00:41
MHDA- 2023/09/08 06:43
CRDT- 2023/09/06 20:58
PHST- 2022/12/26 00:00 [received]
PHST- 2023/05/23 00:00 [revised]
PHST- 2023/06/24 00:00 [accepted]
PHST- 2023/09/08 06:43 [medline]
PHST- 2023/09/07 00:41 [pubmed]
PHST- 2023/09/06 20:58 [entrez]
AID - S0933-3657(23)00132-X [pii]
AID - 10.1016/j.artmed.2023.102618 [doi]
PST - ppublish
SO  - Artif Intell Med. 2023 Sep;143:102618. doi: 10.1016/j.artmed.2023.102618. Epub 
      2023 Jul 1.