A novel cluster detection of COVID-19 patients and medical disease conditions using improved evolutionary clustering algorithm star

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers in biology and medicine Ročník 138; s. 104866
Hlavní autori: Hassan, Bryar A., Rashid, Tarik A., Hamarashid, Hozan K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.11.2021
Elsevier Limited
Predmet:
Algorithms
Chromosomes
Clustering
Coronavirus disease 2019
Coronaviruses
COVID-19
Data processing
Datasets
Decision making
Disease
ECA
Elbow
Evolutionary algorithms
Evolutionary clustering algorithm star
Genetic algorithms
iECA
Improved evolutionary clustering algorithm star
Internal Medicine
Medical conditions
Medical research
Methods
Multidimensional data
Mutation
Noise
Other
Performance evaluation
Statistical methods
Stellar evolution
COVID-19
ECA
Medical conditions
Coronavirus disease 2019
Evolutionary clustering algorithm star
iECA
Improved evolutionary clustering algorithm star
ISSN:0010-4825, 1879-0534, 1879-0534
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms. [Display omitted] •Proposing an improved algorithm, called iECA*, for clustering COVID-19 and medical disease conditions.•The performance of iECA* is examined against the state-of-the-art algorithms using performance and clustering validation measures.•iECA* is more effective than its rivals in grouping the medical datasets.•iECA* is the winning algorithm among current clustering methods in less execution time and memory consumption.•An operational framework is proposed to rate the effectiveness of iECA* against its competitors for medical datasets.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2021.104866