Name
Title | ||
|---|---|---|
Heterogeneous machine learning system for improving the diagnosis of primary aldosteronism |
Abstract | ||
|---|---|---|
•Novel classifier for the diagnosis of Aldosterone-Producing Adenoma.•Improved accuracy (as measured by EUC metric) with respect to state of art.•Feature correlation, missing values and class imbalance all effectively managed.•Validation through a novel, robust technique over a large dataset of patients. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.patrec.2015.07.023 | Pattern Recognition Letters |
Keywords | Field | DocType |
Primary aldosteronism,Ensemble of classifiers,Missing values,Feature correlation,Class imbalance | Signal processing,Pattern recognition,Wilcoxon signed-rank test,Correlation,Software,Artificial intelligence,Missing data,Classifier (linguistics),Cross-validation,Machine learning,Mathematics,Test set | Journal |
Volume | Issue | ISSN |
65 | C | 0167-8655 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Lazzarini | 1 | 31 | 2.34 |
| Loris Nanni | 2 | 1972 | 109.58 |
| Carlo Fantozzi | 3 | 55 | 5.68 |
| Andrea Pietracaprina | 4 | 131 | 16.33 |
| Geppino Pucci | 5 | 443 | 50.49 |
| Teresa Maria Seccia | 6 | 0 | 0.34 |
| Gian Paolo Rossi | 7 | 390 | 78.09 |