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The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.
Ma, Mingrui; Tian, Xuecong; Chen, Fangfang; Ma, Xiaojian; Guo, Wenjia; Lv, Xiaoyi.
Afiliación
  • Ma M; Department of Information Management, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China.
  • Tian X; College of Software, Xinjiang University, Urumqi, 830046, China.
  • Chen F; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
  • Ma X; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China.
  • Guo W; Department of Information Management, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China.
  • Lv X; Institute of Cancer, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China. wenjia510@126.com.
Lasers Med Sci ; 37(2): 1007-1015, 2022 Mar.
Article en En | MEDLINE | ID: mdl-34241708
The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Máquina de Vectores de Soporte / Glioma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Lasers Med Sci Asunto de la revista: BIOTECNOLOGIA / RADIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Máquina de Vectores de Soporte / Glioma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Lasers Med Sci Asunto de la revista: BIOTECNOLOGIA / RADIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido