MC-Transaction on Biotechnology, 2012, Vol.4, No.1 e4
On 2D Cerebellar Model-Based Heart-Disease Classification System
Jia-Ling Lee1, *, Steven Tsai2
1 Department of Biomedical Engineering, School of Health Technology, Ming-Chuan
University, (Taoyuan, Taiwan, R.O.C.)
2 Department of Electronic Engineering, School of Information Technology,
Ming-Chuan University, (Taoyuan, Taiwan, R.O.C.)
Received 3 Aug 2012/Revised 30 Aug 2012/Accepted 31 Aug 2012/Online published 7 September 2012
Abstract
We will propose a classification system using two dimensional Cerebellar Model (2D CM) for the heart-disease database of Porto university. The purpose of this paper is to classify some classes of those patients in the database into the correct one. The design procedure for the2D CM classification system is as follows: (1) We select some samples whose heart disease may be absent or present from the database randomly, and compose these samples to act as a training group and an evaluated group. Each group contains both samples with or without heart disease. (2) The classification system is then schemed. (3) Based on the desired output and the learning rule, the weighting memory of CM is tuned from the training group. (4) The classification system is tested from the evaluated group finally. In this paper, we use both the blocks’ intersection method to build up address indices rapidly, and the coordinate computing method to connect states with address indices. We use two types of different attributes in the database to act as the input of the classification system, and adopt the mean output error method for the learning rule to tune the weighting memory cell of CM. The proposed framework of 2D CM classification system in this paper is simple and converges fast within 1% output error. In the evaluated trial, we find two important attributes, whose relation level with heart disease is more higher, to jointly screen the potential heart disease. The percentage of accurate classification rate can attain 81.5%, and the percentage of capture rate for those who have heart disease can attain 96.2%. It is demonstrated that the proposed classification scheme in this paper is effective for the 2D CM framework.Furthermore, we find that the better classification result can be achieved by adopting some attributes with higher relation level and with the same tendency towards heart disease.
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