| Autor: |
GUHAN, T., REVATHY, N., JEGADEESWARAN, K., ANURADHA, K. |
| Předmět: |
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| Zdroj: |
Oxidation Communications; 2021, Vol. 44 Issue 1, p149-161, 13p |
| Abstrakt: |
The focal aim of this work was to build up an Elitist streamlined sawtooth genetic algorithm (SAWTGA) for anticipating the danger of coronary illness to a patient with the clinical records got from the patients. The classification was performed by machine learning technique to foresee the danger of heart disease. Thirteen features were given as input to the classifier. The proposed system can be utilised by the specialists to anticipate the illness in the previous stages proficiently with improved accuracy. The viability of the methodology has been assessed with the tuples accessible in the informational index. The results exhibited that the proposed calculation outperforms existing method. SAWTGA classifier can anticipate the likelihood of patients with coronary sickness in a more promising manner. The exploratory outcomes have demonstrated that the proposed approach has accomplished improvement in accuracy. This actualises that the elitist streamlined Sawtooth Genetic Algorithm will be a sought-after classifier that could be helpful guide for the specialists to productively and precisely anticipate the coronary illness with improved accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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