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Fig. 3 | Respiratory Research

Fig. 3

From: Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease

Fig. 3

The Cox regression model with LASSO (Least Absolute Shrinkage and Selection Operator) was adopted to reduce the redundancy of high-dimensional features and to select the most useful prognostic features. The lambda with 1 standard error of the minimum criteria (the 1-SE criteria) by the black line, and the red line equals lambda with the minimum criteria. A λ value of 0.052, with log (λ) of − 2.950 was chosen (the minimum criteria) according to tenfold cross-validation (A). LASSO coefficient profiles of the 74 features. A coefficient profile plot was produced against the log (λ) sequence. Red vertical line was drawn at the value selected using tenfold cross-validation, where optimal λ resulted in 14 nonzero coefficients (B)

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Respiratory Research

ISSN: 1465-993X

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