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Published on: August 24, 2011
Machine learning-based modified BAT score in predicting hematoma enlargement after spontaneous intracerebral
Hongli Zhou1, Zhiming Zhou2, Zuhua Song2
1Department of Radiology, Nanchong Central Hospital, Nanchong 637000, Sichuan, China; Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China.
Objective:
The BAT score is an easy-to-use prediction tool to detect hematoma enlargement after spontaneous intracerebral hemorrhage. Machine learning technique has potential predictive gains in accuracy over regression models. We sought to use machine learning technique to improve the BAT score for the prediction of hematoma enlargement.
Methods:
Totally 232 patients with spontaneous intracerebral hemorrhage were enrolled from our hospital between 2015 and 2020. The BAT score was calculated to identify high-risk patients with hematoma enlargement. Using the same variables of the original BAT score and 5 common machine learning algorithms, the modified BAT scores were constructed in the training subset (n = 162) and validated in the testing subset (n = 70). Receiver operating characteristic curves were performed to evaluate the discriminative ability of all BAT scores.
Results:
Among 5 modified BAT scores, the modified BAT score based on Naive Bayes algorithm performed best, with the area under the receiver operating characteristic curve (AUC) of 0.83 in the training subset and 0.77 in the testing subset. The DeLong test showed that the performances of the modified BAT score based on Naive Bayes algorithm were significantly better than that of the BAT score (AUC = 0.57) in the training and testing subsets (both P < 0.001).
Conclusions:
Machine learning technique could improve the identification performance of the original BAT score using the same variables. The modified BAT score based on Naive Bayes algorithm could be used as an effective prediction tool for identifying high-risk patients with hematoma enlargement.
