Clinical utility of MLP model
renal pathological impairment
Based on the DCA curve, the MLP model outperformed both the treat-none and treat-all strategies across a wide range of risk threshold probabilities, suggesting that the model demonstrates good clinical utility (Decision curve analysis curves for the multilayer perceptron model in the training (A) and test cohorts (B). The y-axis shows the net benefit, and the x-axis indicates the risk threshold. The red line represents the prediction model. The blue line represents the assumption that all patients have moderate-severe renal pathological impairment. The black line depicts the assumption that none of the patients suffer from moderate-severe impairment. The net benefit was calculated by subtracting the proportion of false-positive patients from the proportion of true-positive patients, weighted by the relative harm of forgoing treatment compared with the negative consequences of unnecessary treatment.Clinical impact curves of the multilayer perceptron model in the training (A) and test cohorts (B). The y-axis measures the number of individuals at high risk, and the x-axis measures the risk threshold. The red curve shows how many out of 1000 patients the prediction model classifies as positive (high-risk) at each probability threshold. In contrast, the blue curve shows the number of true positives at each probability threshold.