RESUMO
Objective To determine the influencing factors of post-stroke depression by machine learning.Methods Stroke patients' medical records (688 cases eligible) were extracted from record system,including age,gender,pulse manifestation,complexion,tongue quality,fur,Chinese medicine intervention,body mass index (BMI),blood pressure,blood glucose,blood triglyceride,blood total cholesterol,smoking history,drinking history,depression family history,stroke lesion site in imaging,as well as the final depression judgment.Single rule algorithm (1R) was adopted to learn.The risk factors influencing post-stroke patients' depression in extracted information were determined.Then the cases collected were divided into the training dataset (500 cases) and the test dataset (188cases).Optimal discriminant results were obtained by random forest model.Results Single rule algorithm showed that the most important influencing factor of post-stroke depression was stroke lesion site.By computer speculation,stroke lesions in the frontal and temporal lobes were most prone to post-stroke depression.Basal ganglia,brain stem,cerebellum,medulla oblongata and occipital lobe lesions were less likely to cause depression.The accurate classification rate could amount to 88.95％ (612/688 cases).Random forest model determination was made in the former 500cases in the training dataset.The total correct rate of determining depression was 98.2％.The total correct rate of determination in 188 cases of the test dataset was 99.47％.Six hundred and eighty-eight patients' data were learnt by random forest model.The total correct rate was 98.84％.The importance measure results showed that top 3 important indexes of post-stroke depression were lesion site,Chinese medicine intervention and depression family history.Conclusion Patients with lesions in the frontal & temporal lobes and depression family history were most prone to post-stroke depression.