Heuristics for interesting class association rule mining a colorectal cancer database

• Published in: Information processing & management 2020-05, Vol.57 (3), p.102207, Article 102207
• Main Authors: Delgado-Osuna, José A., García-Martínez, Carlos, Gómez-Barbadillo, José, Ventura, Sebastián
• Format: Article
• Language: English
• Subjects: Algorithms; Cancer; Class association rule mining; Colorectal cancer; Data mining; Heuristic; Heuristic operators; Interesting association rule mining
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2020.102207

•Heuristic operators are proposed for interesting class association rule mining.•Our proposal remains focused and avoids the exponential curse of other alternatives.•Generated rule sets are more attractive for the subsequent expert inspection.•Interesting descriptions of certain colorectal cancer cases were obtained. [Display omitted] Colorectal cancer affects many people and is one of the most frequent causes of cancer-related deaths in many countries. Professionals of the Reina Sofia University Hospital have fed a database about this pathology, with 1516 patients and 126 attributes, for more than 10 years. Finding useful knowledge therein has shown to be a difficult endeavor. We present four heuristic operators and a complete methodology for searching for interesting rules that describe cases with complications and recurrences. Our proposal has shown some advantages over the well-known Apriori algorithm, for class association rule mining, and the adaptation of three representatives of associative classification. Besides, it has allowed us to identify rules with practical interest among the vast amount of trivial and sporadic associations.