Intracerebral Hemorrhage Detection in Computed Tomography Scans Through Cost-Sensitive Machine Learning

Intracerebral hemorrhage is the most severe form of stroke, with a greater than 75% likelihood of death or severe disability, and half of its mortality occurs in the first 24 hours. The grave nature of intracerebral hemorrhage and the high cost of false negatives in its diagnosis are representative of many medical tasks. Cost-sensitive machine learning has shown promise in various studies as a method of minimizing unwanted results. In this study, 6 machine learning models were trained on 160 computed tomography brain scans both with and without utility matrices based on penalization, an implementation of cost-sensitive learning. The highest-performing model was the support vector machine, which obtained an accuracy of 97.5%, sensitivity of 95% and specificity of 100% without penalization, and an accuracy of 92.5%, sensitivity of 100% and specificity of 85% with penalization, on a test dataset of 40 scans. In both cases, the model outperforms a range of previous work using other techniques despite the small size of and high heterogeneity in the dataset. Utility matrices demonstrate strong potential for sensitive yet accurate artificial intelligence techniques in medical contexts and workflows where a reduction of false negatives is crucial.