Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods

Abstract

This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices, a feature selection scheme is applied to select the most informative features. Support vector machine (SVM) is used to train the data and build the prediction model. The validation is performed with 10 fold cross validation. To avoid overfitting, all the feature selection and parameter selection are done using training data during the 10 fold cross validation for evaluation. This results an nested cross validation scheme implemented using Rapidminer. The final classification result shows the effectiveness of the proposed method in ICP prediction.