Isolated pulmonary nodule malignant risk prediction system based on logistic regression model
A logistic regression model and risk prediction technology, applied in the field of medical diagnosis, can solve the problems of overestimating the risk of lung cancer, poor matching, poor malignant risk prediction accuracy, etc. Effect
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[0049] Example 1: Logistic Regression Model
[0050] The data of 721 subjects were used for modeling, of which malignant and benign cases were 522 and 199, respectively. The modeling group and the validation group were allocated 3:1. There were 541 cases in the modeling group (392 malignant cases and 149 benign cases) and 180 cases in the validation group (130 malignant cases and 50 benign cases).
[0051] Logistic regression was used to screen for risk factors, and since all parameters in the training group were tested by univariate analysis, we directly employed stepwise logistic regression to identify independent predictors of malignant nodules. Finally, the established model formula is:
[0052] Nodule malignancy probability f(x)=e x / (1+e x );
[0053] Among them, x=-2.8107+(1.2454×with or without smoking history)+(-1.4055×CT nodule with smooth border)+(0.0771×age)+(-1.0728×sex)+(-0.6228×CT nodule with clear border) +(-1.3319×CT nodule calcification)+(0.6890×whether ...
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