Efficient hybrid algorithm based on genetic with weighted fuzzy rule for developing a decision support system in prediction of heart diseases
| Autor: | Abdul Hameed, Ahmed A. Bakhsh, Muhammad Atif Shahzad, Balamurugan Ramasamy |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
020203 distributed computing
Decision support system Fuzzy rule business.industry Computer science Feature selection 02 engineering and technology Machine learning computer.software_genre Hybrid algorithm Fuzzy logic Clinical decision support system Theoretical Computer Science Hardware and Architecture Genetic algorithm 0202 electrical engineering electronic engineering information engineering Artificial intelligence Sensitivity (control systems) business computer Software Information Systems |
| Zdroj: | The Journal of Supercomputing. 77:10117-10137 |
| ISSN: | 1573-0484 0920-8542 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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