Establishment of naive Bayes classifier-based risk prediction model for chemotherapyinduced nausea and vomiting

Abstract

To establish a risk prediction model of chemotherapy-induced nausea and vomiting based on naive Bayes classifier. We collected the basic information, treatment protocols and follow-up data from 300 patients receiving chemotherapy in the Oncology Department of Second Xiangya Hospital from July to September, 2020. Correlation analysis was carried out between the potential factors related to nausea and vomiting in the treatment plan and the individual characteristics of the patients. For the two characteristics with a correlation coefficient greater than 0.8, their contribution to the area under curve (AUC) was calculated, and the characteristic with a smaller contribution was removed. The naive Bayes classifier in the machine learning library scikit-learn was used as the prediction model of chemotherapy-induced nausea and vomiting, and 10-fold stratified-shuffled-split cross-validation was used to obtain the final result of the model. The machine learning model was trained using 70% of the samples, and 30% of the samples were used as the test set to assess the performance of the model. The sensitivity of the model for predicting the risk of nausea and vomiting due to acute chemotherapy was 0.83±0.04 (95%CI: 0.80-0.86) with a specificity of 0.45±0.03 (95%CI: 0.42-0.47) and an AUC of 0.72±0.04 (95% CI: 0.69-0.75). The sensitivity of the model for predicting the risk of delayed chemotherapy-induced nausea and vomiting was 0.84±0.01 (95%CI: 0.83-0.86) with a specificity of 0.48±0.03 (95%CI: 0.45-0.52) and an AUC of 0.74±0.02 (95%CI: 0.72-0.77). The naive Bayes classifier model has a good performance in predicting the risk of chemotherapy-induced nausea and vomiting in Chinese cancer patients.