Establishment and evaluation of early prognosis models of acute intracerebral hemorrhage based on support vector machine
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    Abstract:

    Objective:To compare the performance of predictive models which were established by support vector machine(SVM)and traditional logistic regression and to study the new method of early prognosis in the patients with ICH. Methods:Totally 339 patients with ICH were collected and followed up the clinical outcomes for 21 days. Using the random number method,the original sample was divided into two groups according to the proportion of 3∶1. One group(254 cases) was regarded as a training set for screening the variables and establishing the prediction model and the another group(85 cases) was used as validation set for evaluating the model effect. SVM and the conventional statistical methods of logistic regression were used to construct the predictive models. Results:Through the discriminant validation of the forecast of 85 patients with ICH,the predictive ability of SVM1 was the strongest in the four models. The accuracy and Youden index of four models were as follows,logistic regression:72.9%(62.0%~81.7%),0.441 (0.249~0.633);SVM1:82.4%(72.3%~89.5%),0.632(0.465~0.799);SVM2:78.8%(68.4%~86.6%),0.557 (0.379~0.735);SVM3:78.8%(68.4%~86.6%),0.563 (0.385~0.741). Conclusion:The model based on SVM could better predict the early prognosis of the patients with ICH. The efficacy of SVM model is superior to that of logistic regression model.

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张丽娜,李国春,周学平,吴勉华,金妙文,周仲瑛,过伟峰,叶 放,陈诗娴,王延辰,周 玲.基于支持向量机的急性出血性脑卒中早期预后模型的建立与评价[J].南京医科大学学报(自然科学版英文版),2016,(1):80-89.

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History
  • Received:June 08,2015
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  • Online: January 30,2016
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