Authors:
Jongseong Kim
and
Hoo-Gon Choi
Affiliation:
Sungkyunkwan University, Korea, Republic of
Keyword(s):
Diagnosis Model, Genetic Programming, Absorbing Evolution, Accuracy and Recall Rate, Computing Time.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
An accurate diagnosis model is required to diagnose the medical subjects. The subjects should be diagnosed with high accuracy and recall rate by the model. The laboratory test data are collected from 953 latent subjects having type 2 diabetes mellitus. The results are classified into patient group and normal group by using support vector machine kernels optimized through genetic programming. Genetic programming is applied for the input data twice with absorbing evolution, which is a new approach. The result shows that new approach creates a kernel with 80% accuracy, 0.794 recall rate and 28% reduction of computing time comparing to other typical methods. Also, the suggested kernel can be easily utilized by users having no and little experience on large data.