Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
Med Eng Phys. 2010 Jan;32(1):83-9. doi: 10.1016/j.medengphy.2009.10.011. Epub 2009 Nov 18.
This article is concerned with soft computing-based noninvasive screening for malignant disorders in human larynx. The suitability of two types of data for the analysis is explored. The questionnaire data and the digital voice recordings of the sustained phonation of the vowel sound /a/ are the data types considered in this study. The screening is considered as a task of data classification into the healthy, cancerous, and noncancerous classes. To explore data and decisions a nonlinear mapping technique exhibiting the property of local data ordering is applied. The classification accuracy of over 92% was obtained for unseen questionnaire data collected from 240 subjects. The experimental investigations have shown that, concerning the three classes, the questionnaire data carry much more discriminative information than the voice signal. Two-dimensional plots created using the mapping technique provide further insights into the data and decisions obtained from the classifiers.
本文探讨了基于软计算的人类喉恶性疾病的无创筛查。研究探索了两种类型的数据的适用性。本研究考虑了问卷数据和元音/a/持续发音的数字语音记录这两种数据类型。筛查被视为将数据分类为健康、癌症和非癌症类别的任务。为了探索数据和决策，应用了一种具有局部数据排序属性的非线性映射技术。对于从 240 名受试者中收集的未见过的问卷数据，获得了超过 92%的分类准确性。实验研究表明，就这三个类别而言，问卷数据比语音信号具有更多的判别信息。使用映射技术创建的二维图提供了对分类器获得的数据和决策的进一步了解。
