| Publisher | Tulane University | ||
|---|---|---|---|
| Format | 295.6KB PDF | Date added | 18 Jun 2002 |
| Topics | Knowledge and Data Management, Data Mining - Analysis, Database Management | ||
| Downloads | 1 | ||
The publicly available Pima Indian Diabetic Database (PIDD) at the UC-Irvine Machine Learning Lab has become a standard for testing data mining algorithms to see their accuracy in predicting diabetic status from the 8 variables given. Looking at the 392 complete cases, guessing all are non-diabetic gives an accuracy of 65.1%. Since 1988, many dozens of publications using various algorithms have resulted in accuracy rates of 66% to 81%. Rough sets as a data mining predictive tool has been used in medical areas since the late 1980s, but not applied to the PIDD to one's knowledge. When one applies rough sets to PIDD using ROSETTA software, there are many different options within the software to choose from.
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