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首页> 外文期刊>BMC Medical Informatics and Decision Making >Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies
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Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

机译：支持向量机与逻辑回归模型对血液系统恶性肿瘤危重患者住院死亡率的预测

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Background Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [ 1 ]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models. Methods 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (± SE). Results The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (± 0.04) vs. 0.802 (± 0.04) in the first model (p = 0.19) and 0.781 (± 0.05) vs. 0.808 (± 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.
机译：背景技术近年来，已经为血液病恶性肿瘤的重症患者建立了几种死亡率预测模型。这些模型已被证明与ACUCHE II或SAPS II等ICU疾病严重程度评分相比，在预测血液系统恶性肿瘤患者的医院死亡率方面具有同等或更准确的效果[1]。这项研究的目的是在基于多对数回归（MLR）的模型与基于支持向量机（SVM）的模型之间，比较预测入住ICU的血液系统恶性肿瘤患者的医院死亡率预测的准确性。方法纳入1997年至2006年间因危及生命的并发症而入住ICU的352例血液系统恶性肿瘤患者。 252条患者记录用于模型训练，100条用于验证。在第一个模型中，包括了12个输入变量，用于MLR和SVM之间的比较。在第二个更复杂的模型中，使用了17个输入变量。 MLR和SVM分析彼此独立进行。使用接收器工作特性（ROC）曲线（±SE）下的面积评估歧视。结果验证数据集中MLR和SVM的ROC曲线下面积分别为第一个模型（p = 0.19）中的0.768（±0.04）vs. 0.802（±0.04）和0.781（±0.05）的0.881（±0.04） ）在第二个更复杂的模型中（p = 0.44）。支持向量机仅需要4个变量就可以在两个模型中进行预测，而MLR在第一个模型和第二个模型中分别需要7个变量和8个变量。结论MLR和SVM模型的判别力都很好。在MLR和SVM之间的判别力在预测血液系统恶性肿瘤危重患者住院死亡率方面没有统计学上的显着差异。

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