Cervical cancer is one of the most common female malignant tumors in the world. In recent years, the incidence of cervical cancer has tended to be younger, which has attracted great attention from all countries in the world. Early and accurate diagnosis of cervical cancer is of great significance to patients. At present, the common diagnostic methods of cervical cancer in China include cytological screening and HPV detection, but these methods are generally greatly affected by doctors' subjective factors and cannot fully meet the domestic clinical needs, so a rapid and accurate diagnosis of cervical cancer is of great value for exploration and research. In this paper, serum infrared spectroscopy technology combined with machine learning was used to diagnose and classify cervical cancer patients. Firstly, the spectral data were preprocessed by smoothing and normalization, and principal component analysis (PCA) was used to reduce dimension. The obtained data were imported into Support Vector Machines with Particle Swarm Optimization (PSO-SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) models for classification, and ten-fold cross-validation was used to verify the performance of the model. Finally, the established models are compared.
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.
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