title: The prediction of malignant middle cerebral artery infarction: a predicting approach using random forest.
abstract: BACKGROUND
  Malignant middle cerebral artery infarction (MMI) is always associated with high mortality rates. Early decompressive craniectomy is crucial to its treatment. The purpose of this study was to establish a reliable model for an early prediction of MMI.
  
  
  METHODS
  Using a retrospective survey, we have collected the data of 132 patients with middle cerebral artery infarction. According to a prognosis, the patients are divided into the MMI group (n = 36) and the non-MMI group (n = 96). All the patients are represented by their clinical, biochemical, and imaging features. Then a random forest (RF) prediction model is established on the clinical data. Meanwhile, 3 traditional prediction models, including univariate linear discriminant analysis (LDA) model, multivariate LDA model, and binary logistic regression analysis (BLRA), are built to compare with the RF model. The prediction performance of different models is assessed by the area under the receiver operating characteristic curves (AUCs).
  
  
  RESULTS
  Four parameters, Glasgow Coma Scale, midline shifting, area, and volume of focus, selected as predictors in all models. As independent predictors, their AUCs are .72-.80, and when the sensitivities are high (.91-.95), the specificities are low (.32-.53). The AUC of RF model is .96, 95% confidence interval (CI) is (.93-.99), sensitivity is 1, and specificity is .85. The AUC of the multivariate LDA model is .87 and 95% CI is (.80-.93). The AUC of the BLRA model is .86 and 95% CI is (.80-.93).
  
  
  CONCLUSIONS
  The RF performs very well in the given clinical data set, which indicates that the RF is applicable to the early prediction of the MMI.
authors: Neurological Department, The Third Xiangya Hospital of Central South University, Hunan, China.
authors: Ru
authors: R
authors: Chen
authors: Department of software engineering, School of Computer and Communication Engineering, Changsha University of Science and Technology, Hunan, China.
authors: Zelin
authors: Z
authors: Deng
authors: Neurological Department, The Third Xiangya Hospital of Central South University, Hunan, China. Electronic address: doc_sz@126.com.
authors: Zhi
authors: Z
authors: Song
mesh_terms: D000368:Aged; D016002:Discriminant Analysis; D005260:Female; D015600:Glasgow Coma Scale; D006801:Humans; D007091:Image Processing, Computer-Assisted; D020244:Infarction, Middle Cerebral Artery / Q000175:diagnosis*; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies
keywords: Brain edema; acute stroke; decompressive craniectomy; random forest; risk prognostication