The exponential growth of medical data has generated the need to implement new techniques of information analysis that support decision making. The objective of this article is to identify the aggregated value that data mining models have in decision making in the information given by exploratory analysis. It was used a case study methodology for two data sets, that look to determine the malignity of detected masses, in the breasts of patients, through the interpretation of attributes registered from the mases. The results show a complementary behavior of both techniques.
|Título traducido de la contribución||Evaluation of models of decision trees and K-means models in the characterization or diagnosis of some diseases|
|Estado||Publicada - 1 jun. 2018|
- Breast cancer
- Decision Trees
- K-means clustering