We describe a data mining model for constructing an optimal diagnostic sequence that assists cost-effective sequential decisions. We use heuristic search, i.e., hill climbing and genetic algorithms (GAs), and the evaluation function of cost-based Mean Accuracy Gain (cMAG), which is provided by SVM classifiers, to find this optimal sequence. GA can find a good sequence because of the ability to escape from local optima.
|Original language||English (US)|
|Number of pages||1|
|Journal||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|State||Published - 2008|