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Rapid diagnosis and classification of cervical lesions by serum infrared spectroscopy combined with machine learning

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.