Abstract: To automatically recommend machine learning models for auxiliary diagnosis of diseases to doctors, a screening method of machine learning model is proposed. The screening method consists of three steps including screening machine learning models with training accuracy and testing accuracy, screening machine learning models with precision rates, recall rates and F1 scores, recommending the optimal machine learning model using the total score formula with weights. Taking the auxiliary diagnosis of breast cancer as an example, the support vector machine model （γ=0.5） based on Gaussian RBF (Radial Basis Function) is finally selected among 8 machine learning models and recommended for doctors to use, because in addition to meeting the three conditions of the screening method, the model achieves the highest total score of 0.985.

Key words: machine learning model, auxiliary diagnosis, screening method, learning curve, model score