Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.
Recommender Systems have recently undergone an unwavering improvement in
terms of efficiency and pervasiveness. They have become a source of
competitive advantage in many companies which thrive on them as the
technological core of their business model. In recent years, we have made
substantial progress in those Recommender Systems outperforming the accuracy
and added-value of their predecessors, by using cutting-edge techniques such
as Data Mining and Segmentation. In this paper, we present AKNOBAS, a
Knowledge-based Segmentation Recommender System, which follows that trend
using Intelligent Clustering Techniques for Information Systems. The
contribution of this Recommender System has been validated through a business
scenario implementation proof-of-concept and provides a clear breakthrough of
marshaling information through AI techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.