From: A novel approach for heart disease prediction using strength scores with significant predictors
Authors | Technique | No of Features Used | Evaluation Metric | Score | Dataset |
---|---|---|---|---|---|
AkbaÅŸ et al. [3] | Associative Rule Mining | 13 | Confidence | 97.8 (Predicting no heart disease) | UCI |
Vasanthanageswari and Vanitha [54] | Associative Rule Mining | 16 | NA | NA | Congenital Heart Defect Dataset |
Shuriyaa and Rajendranb [42] | Associative Rule Mining + ANFIS | 13 | Accuracy | 93.2 | UCI |
Sonet et al. [45] | Associative Rule Mining | 13 | Confidence | 99 | National Institute of Cardiovascular Disease, Dhaka, Bangladesh |
Thanigaivel and Kumar [52] | Associative Rule Mining | 25 | Confidence | 100 | Hospital (name of the hospital not mentioned) |
Srinivas et al. [49] | Associative Rule Mining and MLP | 13 | Accuracy | 84.9 | UCI |
Khare and Gupta [24] | Associative Rule Mining | 13 | Confidence | 94 | UCI |
Lakshmi and Reddy [27] | Associative Rule Mining | 13 | Accuracy | 96.6 | UCI |
Said et al. [41] | Associative Rule Mining | 13 | Confidence | 91 | UCI |
Nahar et al. [36] | Associative Rule Mining | 13 | Confidence | 96 | UCI |