Automated Intracranial Pressure Prediction Using Multiple Features Sources

Raised intracranial pressure (ICP) causes serious problem on traumatic brain injury patient. Automated and non-intrusive ICP level prediction saves cost and enhances efficiency. An automated ICP level prediction model based on machine learning method is proposed in this paper. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are selected, extracted and aggregated using different methods. Some demographic information, such as age and injury severity score, is also considered as candidate features. After the feature aggregation, the most important features are selected by a feature selection scheme applied on 10 fold nested cross validation. The final support vector machine classification result using RapidMiner shows the effectiveness of the proposed method in ICP level prediction.