Table 12 Comparative Analysis of Weighted Associative analysis and Associative Rule Mining in predicting heart disease
From: A novel approach for heart disease prediction using strength scores with significant predictors
Research | Confidence Score (%) | Rules | No of attributes in highest confidence rule | Technique | Dataset |
|---|---|---|---|---|---|
Nahar et al. [36] | 96 | Chest_Pain_Type = asympt, Slope = flat, Thal = rev | 3 | ARM | UCI |
Said et al. [41] | 91 | Chest Pain Type = asymptomic and Thal = reversible defect | 2 | ARM | UCI |
Khare and Gupta [24] | 94 | Thal = reversible_defect, CP = asymptomatic, Exercise_Induced_Angina = yes | 3 | ARM | UCI |
Sonet et al. [41] | 97 | Lack-of-Exercise = yes, Stress = yes, BP = high, Smoking = yes, Diabetes = yes ֜ | 5 | ARM | Data collected from 4 medical institutions (131 records) |
99 | Diabetes | 1 | ARM | ||
Soni and Vyas [48] | 79.5 | NA | NA | WARM | UCI |
Soni et al. [46] | 80 | NA | NA | WARM | UCI |
Sundar et al. [50] | 84 | NA | NA | WARM | UCI |
Ibrahim & Sivabalakrishnan [18] | 67 | 70..79- > yes | 1 | WARM | UCI |
Our Experiment (all features) | 96 | CP = asymptomatic Slope = flat Thal = reversable | 3 | WARM | UCI |
Our Experiment (8 Significant features) | 98 | CP = asymptomatic, Exang = Yes, Oldpeak = greaterThanZero, Thal = reversible | 4 |