Machine Learning Prediction Models for Different Stages of Non-small Cell Lung Cancer Based on Tongue and Tumor Marker

Abstract Objective To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. Methods Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, five classifiers, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. Results There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. The number of index with statistically significant correlations between tongue feature and tumor marker in the advanced NSCLC group was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. The classifier of neural network based on the tongue feature & tumor marker & baseline data can well predict NSCLC at different stages, the accuracy rates of the five classifiers neural network, random forest, logistic regression, SVM, and naive bayes were 79.69%, 75.00%, 72.81%, 74.06%, 76.56%, and the ROCs were 0.8639, 0.8325, 0.8147, 0.8127, and 0.7969, respectively. Conclusions There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. To some extent, tongue feature, tumor marker, and baseline data could be combined to predict NSCLC at different stages. This study established a new methodological reference for the diagnosis of NSCLC at different stages, but more research with a larger sample size was still required in the future.