Optimizing strategies of Raman spectra model combining pre-processing and classification for diagnosis of lung cancer

Abstract

The analysis of biological sample data based on Raman spectroscopy involves applying the relevant chemometrics pre-processing and creating the statistical model. In this work, genetic algorithms and pipelines were used to study the steps and sequences of the pre-processing for human blood serum Raman spectra form 76 healthy individuals and 84 lung cancer patients for different data analysis models. The models used in this study include support vector machine (linear kernel and nonlinear kernel), multilayer perceptron, and partial least squares discriminant analysis. The results show that the steps and sequence of pre-processing are not immutable for different models. These optimized pipelines are evaluated by execution time and optimization results. The conclusions are that genetic algorithms can optimize the pipeline of pre-processing strategies and classification models to improve data analysis accuracy, and support vector machine models are more suitable for the classification of our lung cancer serum data.