To establish a simple and explainable model and provide reference for improving the accuracy of model prediction in similar research.Methods:The data(heart failure grade and related factors) of 200 patients with pulmonary fibrosis complicated with heart failure in a research project supported by the National Natural Science Foundation of China(81673904) were collected.The Lasso regression was employed to screen out the characteristic variables from sex，age，body mass index(BMI)，systolic blood pressure(SBP)，diastolic blood pressure，total cholesterol(TC)，fasting blood glucose，tongue color，tongue coating color，and traditional Chinese medicine(TCM) physique.A regression model was established to explore the relationship between the severity of heart failure and the characteristic variables.Results:After removal of the confounding factors in high-dimensional data，six characteristic variables were selected for the model，including BMI(0.006 357 091)，SBP(0.219 695 622)，TC(0.229 324 833)，red tongue(0.004 216 705)，thin white tongue coating(-0.825 660 057)，and thick yellow tongue coating(0.356 499 153).The probability of severe heart failure was P＝-33.632+0.006×BMI+0.220×SBP+0.229×TC+0.004×red tongue-0.826×thin white tongue coating+0.356×thick yellow tongue coating.Conclusion:The established model can explain the factors associated with severe heart failure and be applied to the prediction in the population.Lasso regression model is suitable for high-dimensional data analysis of TCM clinical research，demonstrating the value for promotion.