Fig. 3 | Scientific Reports

Fig. 3

From: Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning

Fig. 3

Machine learning screening DFU characteristic correlation factors. LASSO algorithm (A, B) obtained 20 characteristic correlation factors. The dashed line on the left is λ min, and the corresponding λ of this dashed line is 20, which means that under this λ value, the model deviation is minimum and the fitting effect is highest. Results of SVM algorithm (C, D) obtained 14 characteristic correlation factors. The accuracy was highest when 14 factors were used, and the error rate was lowest when 14 factors were used. RF algorithm (E, F) ranks the importance of all correlation factors included. The horizontal coordinate represents importance. Combined with clinical significance, this study selected a total of 12 relevant factors with an importance greater than 10. Intersection of the results of three machine learning algorithms to obtain 10 related factors (G).

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