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The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma

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Abstract

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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Funding

This work was supported by the special scientific research project for young medical science (2019Q003), Xinjiang Uygur Autonomous Region Science and Technology Branch Project of China (2019E0282) and National Natural Science Foundation of China (Grant No. 81760444).

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Author notes

  1. Mingrui Ma and Xuecong Tian contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Department of Information Management, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China

    Mingrui Ma & Xiaojian Ma

  2. College of Software, Xinjiang University, Urumqi, 830046, China

    Xuecong Tian & Xiaoyi Lv

  3. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Fangfang Chen & Xiaoyi Lv

  4. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China

    Fangfang Chen & Xiaoyi Lv

  5. Institute of Cancer, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China

    Wenjia Guo

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  1. Mingrui Ma
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Correspondence to Wenjia Guo or Xiaoyi Lv.

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All procedures performed in this study involving human participants were in accordance with the ethical standards of the Ethics Committee of the Affiliated Tumor Hospital of Xinjiang Medical University.

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Ma, M., Tian, X., Chen, F. et al. The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma. Lasers Med Sci 37, 1007–1015 (2022). https://doi.org/10.1007/s10103-021-03346-6

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  • DOI: https://doi.org/10.1007/s10103-021-03346-6

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