Prediction of postoperative recovery based on a computational rules extractor

Author

Yi-Zeng Hsieh ; Chen-Hsu Wang ; Mu-Chun Su ; Ching-Hu Lu ; Jen-Chih Yu ; Yi Min Chiang

Author_Institution

Dept. of Manage. & Inf. Technol., Southern Taiwan Univ. of Sci. & Technol., Tainan, Taiwan

fYear

2015

Firstpage

332

Lastpage

333

Abstract

One important factor for the patients in a postoperative recovery is hypothermia. The doctor must decide whether the patients should be sent to another place with better medical therapy. We therefore adopt the proposed PSO (particle swarm optimization) based Fuzzy classifier to retrieve the crisp rules from the postoperative given medical data from UCI machine learning database, where the rules can be used to assist in doctor diagnosis. The average correct ratio of our prediction for the postoperative recovery is about 84%.

Keywords

biothermics; classification; fuzzy systems; knowledge based systems; learning (artificial intelligence); medical diagnostic computing; particle swarm optimisation; patient diagnosis; patient treatment; PSO based fuzzy classifier; UCI machine learning database; average correct ratio; computational rule extractor; doctor diagnosis assistance; hypothermia; medical therapy; particle swarm optimization; postoperative medical data; postoperative patient recovery; postoperative recovery prediction; Data mining; Fuzzy systems; Medical services; Neural networks; Particle swarm optimization; Testing; Training; fuzzy system; neural network; postoperative recovery;

fLanguage

English

Publisher

ieee

Conference_Titel

Consumer Electronics - Taiwan (ICCE-TW), 2015 IEEE International Conference on

Conference_Location

Taipei

Type

conf

DOI

10.1109/ICCE-TW.2015.7216928

Filename

7216928