Research Article
Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics
Table 4
Best classification scores.
| Score, % | Features |
| Linear discriminant analysis | 91.33 ± 1.75 | HR | VLFn(Fr) | LF/HF(Fr) | VLF(wt) | 90.30 ± 1.37 | HR | VLFn(Fr) | VLF(wt) | (LF/HF)int | 90.04 ± 1.85 | HR | LF/HF(Fr) | VLF(wt) | VLFn(wt) | 90.44 ± 1.60 | HR | VLFn(Fr) | LF/HF(Fr) | SDVLF | 90.11 ± 1.80 | HR | LF/HF(Fr) | SDVLF | VLFn(wt) | 90.16 ± 1.61 | HR | SDVLF | VLFn(wt) | (LF/HF)int |
| Quadratic discriminant analysis | 90.31 ± 1.71 | HR | VLFn(Fr) | LF/HF(Fr) | VLF(wt) |
| 3-nearest neighbors | 87.14 ± 2.12 | LF/HF(Fr) | SDVLF | VLFn(wt) | W1/2VLF |
| 4-nearest neighbors | 85.56 ± 2.40 | SDVLF | VLFn(wt) | LF/HF(wt) | W1/2VLF |
| 5-nearest neighbors | 86.63 ± 1.30 | HR | HF(Fr) | LFn(Fr) | W1/2VLF |
| Support vector machine, radial base function | 86.73 ± 2.24 | IAS | RF | a2LF | WVLF |
| Decision trees, max depth 5 | 87.10 ± 3.40 | IARP | LF/HF(Fr) | IAS | WLF |
| Decision trees, no max depth | 87.34 ± 3.08 | IARP | LF/HF(Fr) | IAS | WLF |
| Naïve Bayes classifier | 88.17 ± 1.07 | VLF(Fr) | VLFn(Fr) | LF/HF(Fr) | W1/2LF |
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