Kernel Generations for a Diagnosis Model with GP

Jongseong Kim, Hoo-Gon Choi

2012

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.

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Paper Citation


in Harvard Style

Kim J. and Choi H. (2012). Kernel Generations for a Diagnosis Model with GP . In Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-18-1, pages 57-62. DOI: 10.5220/0004028500570062


in Bibtex Style

@conference{data12,
author={Jongseong Kim and Hoo-Gon Choi},
title={Kernel Generations for a Diagnosis Model with GP},
booktitle={Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,},
year={2012},
pages={57-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004028500570062},
isbn={978-989-8565-18-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Kernel Generations for a Diagnosis Model with GP
SN - 978-989-8565-18-1
AU - Kim J.
AU - Choi H.
PY - 2012
SP - 57
EP - 62
DO - 10.5220/0004028500570062