OBJECTIVE To provide a dosing basis for clinical antiemetic drug selection by establishing cisplatin chemotherapy-induced nausea and vomiting(CINV) duration prediction model based on machine learning. METHODS The cisplatin CINV duration model was established using support vector machine(SVM), decision tree(DT), naive bayes(NB), random forest(RF), and K near neighbor(KNN) machine learning algorithms with the clinical information of 74 patients who underwent cisplatin-based chemotherapy in our hospital from July 2018 to October 2022 served as its variables detected by principal component analysis(PCA). RESULTS The PCA reduced the dimension of 41 variables, and finally obtained 4 principal components. NB had the highest accuracy, AUC value and sensitivity of 84.21%, 0.916 7% and 100% respectively. RF had the largest F1 value, followed by NB with 86.96% and 85.71% respectively. CONCLUSION The five predictive models established by machine learning, with the NB model performing best, can inform the prevention of cisplatin CINV.
Key words
cisplatin /
machine learning /
nausea /
vomiting /
principal component analysis
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