Near infrared spectroscopy combined with least squares support vector machines and fuzzy rule-building expert system applied to diagnosis of endometrial carcinoma

Fan Yang; Jing Tian; Yuhong Xiang; Zhuoyong Zhang; Peter de B Harrington

doi:10.1016/j.canep.2011.10.009

Near infrared spectroscopy combined with least squares support vector machines and fuzzy rule-building expert system applied to diagnosis of endometrial carcinoma

Cancer Epidemiol. 2012 Jun;36(3):317-23. doi: 10.1016/j.canep.2011.10.009. Epub 2011 Nov 17.

Authors

Fan Yang¹, Jing Tian, Yuhong Xiang, Zhuoyong Zhang, Peter de B Harrington

Affiliation

¹ Department of Chemistry, Capital Normal University, Beijing, China.

PMID: 22094328
DOI: 10.1016/j.canep.2011.10.009

Abstract

Objective: The feasibility of early diagnosis of endometrial carcinoma was studied by least squares support vector machines (LS-SVM) and fuzzy rule-building expert system (FuRES) that classified near infrared (NIR) spectra of tissues.

Methods: NIR spectra of 77 specimens of endometrium were collected. The spectra were pretreated by principal component orthogonal signal correction (PC-OSC) and direct orthogonal signal correction (DOSC) methods to improve the signal-to-noise ratio (SNR) and remove the influences of background and baseline. The effects of modeling parameters were investigated using bootstrapped Latin-partition methods.

Results: The optimal LS-SVM model of the PC-OSC pretreatment method successfully classified the samples with prediction accuracies of 96.8±1.4%.

Conclusions: The proposed procedure proved to be rapid and convenient, which is suitable to be developed as a non-invasive diagnosis method for cancer tissue.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Aged
Endometrial Neoplasms / diagnosis*
Endometrial Neoplasms / pathology
Female
Fuzzy Logic
Humans
Least-Squares Analysis
Middle Aged
Models, Theoretical*
Signal-To-Noise Ratio
Spectroscopy, Near-Infrared / methods*
Young Adult