Abstract: To screen out primary liver cancer patients as early as possible, assist doctors to make better decisions and improve treatment effects, an early screening method based on routine laboratory data is proposed. To test the classification of healthy, benign lesions or primary liver cancer, a support vector machine method is optimized by using a differential evolution algorithm, in which the evaluation cost is the area under the ROC (receiver operating characteristic) curve. Moreover, to satisfy different clinical requirements, performance index curves and cut-off lookup tables of the training model are built, then cut-off values are selected by users to further improve the prediction performance. Compared with other 5 state-of-the-art methods, the proposed methods have better classification performance, of which the accuracy reaches 0.944 1, and the Kappa coefficient reaches 0.903 1. The research results can assist doctors to screen out the primary liver cancer early and improve the long-term survival rate of patients.

Key words: laboratory data, primary liver cancer detection, support vector machine (SVM), differential evolution (DE), area under receiver operation characteristic curve (AUC)