Prediction Models of Ischemic Stroke Using Deep Neural Networks

Abstract

The ischemic stroke is one of the leading causes of death worldwide. Although, there are many known risk factors for the disease the growing amount of electronic medical data available gives opportunities for creating novel models for personal risk prediction. Usage of deep neural network (DNN) for developing such models can offers many benefits such as potential to encode multiple types of data, less feature selection and engineering required, and sometimes even an increased prediction accuracy. This Thesis focuses on developing a model for ischemic stroke prediction using electronic health record (EHR) data. I show that TabNet, a state-of-the art DNN architecture for tabular data analysis outperforms a simpler method, the FastAI tabular learner. Still, neither of the DNN methods achieved better results than the Random Forest. The ensemble models using Random Forest and DNN models were tested but only a small increase in the performance was achieved compared to the singular model. These results indicate that an ensemble-based methods such as Random Forest is sufficient for the data used. Nevertheless, with increased number of features and addition of more complex data types methods such as TabNet could still become valuable. All models developed resulted with high prediction power for ischemic stroke. This indicates that personal risk predictions for ischemic stroke can be given and the clinical utility of the models should be evaluated further.