| Autor: |
Ramani, P., Sugumaran, S., Devi, Manoharan Nivethitha, Nagalakshmi, T.J., Annapoorani, G. |
| Zdroj: |
International Journal of Medical Engineering and Informatics; 2024, Vol. 16 Issue: 5 p424-439, 16p |
| Abstrakt: |
A recent study by the United Nations Agency (World Health Organization) reported that 17.9 million people died due to cardiovascular disease, and it is increasing exponentially. Furthermore, it was also reported that it was highly difficult to recognise the sickness and dictate the relevant care in a timely manner. For analysis, a user data file for cardiopathy prediction that contains parameters such as gender, age, kind of pain, force per unit area, hyperglycaemia, and so on has been considered. The approach entails determining the correlations between the numerous properties of the data file using regular processing techniques and then treating the attributes appropriately to forecast the likelihood of cardiopathy. This article endeavours at probing methodised data-mining techniques such as NB classifier, random forest classification, decision tree in addition to support vector machine. These machine learning approaches require less time to anticipate sickness with a high degree of accuracy. The proposed algorithm provides 91.2% recognition rate than SVM and decision tree classifier. |
| Databáze: |
Supplemental Index |
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