Association Rules Mining on Heart Failure Differential Treatment Based on the Improved Firefly Algorithm
Feng Yuan, Shouqiang Chen, Hong Liu · Journal of Computers · 2014
Research the heart failure medical cases of TCM (traditional Chinese medicine) to effectively mine the association rules of differential diagnosis and treatment. TCM medical cases are of vast amounts of data and strong relatedness, and a new and improved firefly algorithm based on the guide of normative knowledge has been proposed to overcome the shortcomings of traditional association rules mining algorithms with the handling of TCM medical cases data such as low efficiency, slow convergence rate and rules underreporting, etc. The algorithm sets the support degree threshold through the penalty function, adaptively adjusts the hunting zone by normative knowledge to improve the convergence rate and exploration ability of the algorithm; it uses the way of random disturbance to conduct disturbance operation so as to increase the population diversity and effectively avoid algorithm prematurity. Confirmatory experiment of TCM medical cases for the treatment of heart failure has been conducted, the experimental results show that this method has achieved a great improvement on individual diversity and the efficiency of effective rules extraction compared with traditional association rules mining algorithms, and the mining results are of a certain reference value for TCM clinical diagnosis and treatment of heart failure.