Name
Abstract | ||
|---|---|---|
Nowadays, heart disease is one of the important causes of human deaths. According to statistics, deaths caused by heart disease account for about one-third of all deaths in the world. With further research, the use of machine learning to predict heart disease has become an essential method to prevent and treat heart disease. In recent years, machine learning based on big data analysis has been widely used in various software applications, but it has not been used on a large scale in disease prediction. In this article, we propose a new algorithm named hybrid gradient boosting decision tree with logistic regression (HGBDTLR) based on ensemble learning to improve the accuracy of machine learning in heart disease prediction. The actual results prove that the prediction accuracy of HGBDTLR algorithm can reach 91.8% in the Cleveland heart disease data set. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DSA51864.2020.00052 | 2020 7th International Conference on Dependable Systems and Their Applications (DSA) |
Keywords | DocType | ISBN |
Big data analysis,machine learning,heart disease prediction,HGBDTLR algorithm | Conference | 978-1-6654-1953-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Yuan | 1 | 0 | 0.34 |
| Longwei Yang | 2 | 0 | 0.34 |
| Yabing Huang | 3 | 0 | 0.34 |
| Zheng Li | 4 | 0 | 0.34 |