RETRACTED ARTICLE: Efficient hybrid algorithm based on genetic with weighted fuzzy rule for developing a decision support system in prediction of heart diseases

In this article, the clinical decision support system is discussed under the weighted fuzzy rule approach and genetic algorithm for computer-aided heart disease determination. The problem of feature selection is solved by the answers formulated from the stochastic inquiry from the genetic algorithm....

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Bibliographic Details
Published in:The Journal of supercomputing 2021, Vol.77 (9), p.10117-10137
Main Authors: Hameed, Abdul Zubar, Ramasamy, Balamurugan, Shahzad, Muhammad Atif, Bakhsh, Ahmed Atef S.
Format: Article
Language:English
Subjects:
Compilers
Computer Science
Decision support systems
Experimentation
Genetic algorithms
Heart diseases
Interpreters
Machine learning
Mobile and Intelligent Sensing on High Performance Computing
Processor Architectures
Programming Languages
Sensitivity
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Summary:In this article, the clinical decision support system is discussed under the weighted fuzzy rule approach and genetic algorithm for computer-aided heart disease determination. The problem of feature selection is solved by the answers formulated from the stochastic inquiry from the genetic algorithm. In this, the weighed fuzzy framework is built by the application of certain major highlights selected from the datasets. In this, the proposed framework adopted favorable positions by the fuzzy rule strategy and the leaning of the fuzzy approach is being successful by the application of offered weighed methodology activity. At last, the risk forecasting outcomes from the experimentation on UCI machine learning source and supercomputing techniques are assured in our proposed clinical decision support system is enhanced essentially when contrasted with other frameworks in terms of sensitivity specificity, sensitivity, and accuracy.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-03677-9