A new machine learning classifier for high dimensional healthcare data.
Data sets with many discrete variables and relatively few cases arise in health care, commerce, information security, and many other domains. Learning effective and efficient prediction models from such data sets is a challenging task. In this paper, we propose a new approach that combines Metaheuristic search and Bayesian Networks to learn a graphical Markov Blanket-based classifier from data. The Tabu Search enhanced Markov Blanket (TS/MB) procedure is based on the use of restricted neighborhoods in a general Bayesian Network constrained by the Markov condition, called Markov Blanket Neighborhoods. Computational results from two real world healthcare data sets indicate that the TS/MB procedure converges fast and is able to find a parsimonious model with substantially fewer predictor variables than in the full data set. Furthermore, it has comparable or better prediction performance when compared against several machine learning methods, and provides insight into possible causal relations among the variables.
Padman
Rema
R
The H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA 15213, USA. rpadman@cmu.edu
Bai
Xue
X
Airoldi
Edoardo M
EM
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003658:Decision Making, Computer-Assisted; D003695:Delivery of Health Care; D006801:Humans; D008297:Male; D008390:Markov Chains; D016571:Neural Networks, Computer; D011471:Prostatic Neoplasms