Name
Title | ||
|---|---|---|
Diagnosis of liver diseases from P31 MRS data based on feature selection using genetic algorithm |
Abstract | ||
|---|---|---|
P31 MRS technique is important either in diagnosis or in treatment of many hepatic diseases for it can provides non-invasive information about the chemical content of the energy metabolism in cellular level. The data samples from P31 MRS are classified into three types of hepatocellular carcinoma, hepatic cirrhosis and normal hepatic tissue using computational intelligence methods. A genetic algorithm is used as main feature selection method and the Gaussian model is selected in the mutation operation. Two classification algorithms are used which consist of fisher linear discriminant analysis and quadratic discriminant analysis. Experiments show that the application of genetic algorithm and fisher linear classifier offers more reliable information for diagnostic prediction of liver cancer in vivo. And when the cross-validation method is 10-fold model, this algorithm can improve the average recognition correction rate of three types to 94.28%. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15615-1_15 | LSMS/ICSEE |
Keywords | Field | DocType |
p31 mrs,feature selection,hepatic cirrhosis,hepatic disease,genetic algorithm,classification algorithm,gaussian model,p31 mrs technique,liver disease,normal hepatic tissue,computational intelligence method,10-fold model,p31 mrs data,genetics,cross validation,quadratic discriminant analysis,computational intelligence | Pattern recognition,Feature selection,Computational intelligence,Computer science,Gaussian network model,Artificial intelligence,Linear discriminant analysis,Statistical classification,Linear classifier,Genetic algorithm,Machine learning,Quadratic classifier | Conference |
Volume | ISSN | ISBN |
6330 | 0302-9743 | 3-642-15614-2 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinyong Cheng | 1 | 4 | 3.52 |
| Yihui Liu | 2 | 0 | 0.34 |
| Jun Sang | 3 | 40 | 12.62 |
| Qiang Liu | 4 | 0 | 0.68 |
| Shaoqing Wang | 5 | 28 | 5.87 |