Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients (AUC = 0.833, P < 0.001). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training (P < 0.001) and validation datasets (P < 0.005). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.