Application of serum infrared spectroscopy combined with ensemble learning method in rapid diagnosis of cervical lesions

Cervical cancer is one of the major gynecological malignancies that seriously endanger women's health. Patients with early symptoms are not obvious and prone to metastasis and recurrence, leading to poor prognosis of patients with cervical cancer. At present, cytological screening and HPV detection are the main diagnostic methods of cervical cancer in China, but both of them are greatly influenced by doctors' subjective factors, with low specificity and high rate of missed diagnosis. Therefore, a rapid and effective diagnostic method is needed to be explored. In this paper, the serum samples of patients with cervical cancer were taken as the research object, and the experimental serum samples were analyzed by infrared spectroscopy, which provided a clinical basis for the identification and classification of patients with cervical cancer by infrared spectroscopy. In this study, infrared spectral signals of serum of patients with cervical cancer were collected, and spectral signals were analyzed and preprocessed. Partial least squares regression (PLS) was used to select spectral signal features. Then, an Xgboost ensemble learning model is established using GBtree, GBlinear and Dart as the base classifier, and the performance of the model is evaluated by using the ten-dot cross-validation. Finally, the established models are compared.