Objective Logistic regression

decision tree

and LSVM were used to construct a predictive model for early diagnosis of prostate cancer

and the predictive performance of the three models was compared to provide theoretical support for the early diagnosis of prostate cancer. Methods The data were obtained from the prostate tumor data set of the national clinical medical science data center （301 Hospital）. The cleaned data were randomly divided into a training set and testing set according to the proportion of 7:3. Based on the training set data

single factor unconditional Logistic regression was applied to screen the related factors of prostate cancer

and unconditional multivariate Logistic regression analysis and Lagrangian Support Vector Machine （LSVM） model and random forest model were established risk prediction models for prostate cancer. The testing data were used to verify the accuracy of the three models

and ROC curves were used to evaluate their performances. Results The results of single factor logistic analysis showed that 13 indicators

including age

creatine kinase isoenzyme

triglyceride

phospholipid

free PSA

total PSA

calcium

serum uric acid

apolipoprotein A1

apolipoprotein B

apolipoprotein C2

apolipoprotein C3 and apolipoprotein E

were statistically significant. Four variables were selected by multivariate Logistic analysis: age

creatine kinase isoenzyme

free-PSA

total-PSA. LSVM model screened out 10 predictive factors

according to the importance from high to low: total-PSA

age

apolipoprotein A1

phospholipid

apolipoprotein B

triglyceride

serum uric acid

free-PSA

creatine phosphate isoenzyme

and apolipoprotein E. The random forest model selected ten predictive factors

with the order of importance being apolipoprotein C3

phospholipid

free-PSA

apolipoprotein B

apolipoprotein E

calcium

serum uric acid

apolipoprotein A1

apolipoprotein C2

and creatine kinase isoenzyme. The area under the curve （AUC） of multivariate unconditional logistic regression

LSVM model

and random forest model were 0.895（0.876，0.913）

0.918（0.902，0.934） and 0.742（0.688，0.760）

respectively. Conclusion LSVM model had the best predictive performance

followed by the multivariate logistic regression model

and the random forest model had the poorest predictive performance.