Uncertainty rule generation on a home care database of heart failure patients

In this paper we present the uncertainty rule generator tool and the algorithm used. This data-mining tool generates uncertainty rules as apart of the knowledge discovery in databases process and is tested upon a home-care database containing data from congestive heart failure patients over a period of approx. 10 months. This algorithm can handle dynamic data without the need of recovering the itemsets from the beginning. This is highly appropriate for a home-care monitoring system, where new records are constantly added. Moreover it can deal with missing values, since it uses flexible metrics, similar to those of other association rule algorithms. Finally this algorithm computes a certainty factor for each extracted rule, which is representative of its efficiency. In a future step, this extracted rule can be used on newly entered data, in order to predict the missing values, while its certainty factor will allow the exact estimation of error in this prediction.