Authors:
Cecilia Mariciuc
1
;
2
and
Madalina Raschip
1
Affiliations:
1
Faculty of Computer Science, ”Alexandru Ioan Cuza” University of Iasi, General Berthelot 16, Iasi, Romania
;
2
RomSoft, Bulevardul Chimiei 2bis, Iasi, Romania
Keyword(s):
Data Mining, Classification Rules, Particle Swarm Optimization, Disease Detection.
Abstract:
The application of data mining techniques in healthcare is common because the decision-making process for the diagnosis of medical conditions could benefit from the information extracted. A decision system must not only be accurate but also provide understandable explanations for its reasoning. Rule-based models seek to find a small set of rules that can effectively categorize data while providing great human readability. Rule discovery is a complex optimization problem, making it a good candidate for the application of PSO, a versatile, intuitive search algorithm. In this paper, a particle swarm optimization algorithm is used for learning classification rules as part of a Covering-based rule classifier. The proposed PSO is hybridized with the Iterated Local Search metaheuristic, and association rules are used as part of the initialization step. The classifier is tested on several unbalanced medical disease datasets with different types of attributes to more faithfully reflect real-w
orld data. When compared with state-of-the-art rule-based classifiers, the studied algorithm shows good results and is highly interpretable.
(More)